Comparative transcriptome analyses of flower development in four species of Achimenes (Gesneriaceae)

Abstract

Background
Flowers have an amazingly diverse display of colors and shapes, and these characteristics often vary significantly among closely related species. The evolution of diverse floral form can be thought of as an adaptive response to pollination and reproduction, but it can also be seen through the lens of morphological and developmental constraints. To explore these interactions, we use RNA-seq across species and development to investigate gene expression and sequence evolution as they relate to the evolution of the diverse flowers in a group of Neotropical plants native to Mexico—magic flowers (Achimenes, Gesneriaceae). ResultsThe assembled transcriptomes contain between 29,000 and 42,000 genes expressed during development. We combine sequence orthology and coexpression clustering with analyses of protein evolution to identify candidate genes for roles in floral form evolution. Over 25% of transcripts captured were distinctive to Achimenes and overrepresented by genes involved in transcription factor activity. Using a model-based clustering approach we find dynamic, temporal patterns of gene expression among species. Selection tests provide evidence of positive selection in several genes with roles in pigment production, flowering time, and morphology. Combining these approaches to explore genes related to flower color and flower shape, we find distinct patterns that correspond to transitions of floral form among Achimenes species. Conclusions
The floral transcriptomes developed from four species of Achimenes provide insight into the mechanisms involved in the evolution of diverse floral form among closely related species with different pollinators. We identified several candidate genes that will serve as an important and useful resource for future research. High conservation of sequence structure, patterns of gene coexpression, and detection of positive selection acting on few genes suggests that large phenotypic differences in floral form may be caused by genetic differences in a small set of genes. Our characterized floral transcriptomes provided here should facilitate further analyses into the genomics of flower development and the mechanisms underlying the evolution of diverse flowers in Achimenes and other Neotropical Gesneriaceae.

Full text

R E S E A R C H A R T I C L E Open Access
Comparative transcriptome analyses of
flower development in four species of
Achimenes (Gesneriaceae)
Wade R. Roberts
1,2*
and Eric H. Roalson
1,2
Abstract
Background: Flowers have an amazingly diverse display of colors and shapes, and these characteristics often vary
significantly among closely related species. The evolution of diverse floral form can be thought of as an adaptive
response to pollination and reproduction, but it can also be seen through the lens of morphological and developmental
constraints. To explore these interactions, we use RNA-seq across species and development to investigate gene
expression and sequence evolution as they relate to the evolution of the diverse flowers in a group of Neotropical plants
native to Mexico—magic flowers (Achimenes, Gesneriaceae).
Results: The assembled transcriptomes contain between 29,000 and 42,000 genes expressed during development. We
combine sequence orthology and coexpression clustering with analyses of protein evolution to identify candidate genes
for roles in floral form evolution. Over 25% of transcripts captured were distinctive to Achimenes and overrepresented by
genes involved in transcription factor activity. Using a model-based clustering approach we find dynamic, temporal
patterns of gene expression among species. Selection tests provide evidence of positive selection in several genes with
roles in pigment production, flowering time, and morphology. Combining these approaches to explore genes related
to flower color and flower shape, we find distinct patterns that correspond to transitions of floral form among
Achimenes species.
Conclusions: The floral transcriptomes developed from four species of Achimenes provide insight into the mechanisms
involved in the evolution of diverse floral form among closely related species with different pollinators. We identified
several candidate genes that will serve as an important and useful resource for future research. High conservation of
sequence structure, patterns of gene coexpression, and detection of positive selection acting on few genes suggests
that large phenotypic differences in floral form may be caused by genetic differences in a small set of genes.
Our characterized floral transcriptomes provided here should facilitate further analyses into the genomics of
flower development and the mechanisms underlying the evolution of diverse flowers in Achimenes and other
Neotropical Gesneriaceae.
Keywords: Comparative transcriptomics, Flower evolution, Gesneriaceae, Coexpression clustering, RNA-seq
Background
Flowers are a common way that humans connect to na-
ture and the variety of colors and shapes remains one of
the most visible and amazing products of evolution. Inno-
vations in floral form have been proposed as one of the
primary mechanisms of angiosperm diversification [1] and
the phenotypic diversity of flowers is both visually striking
and evolutionarily intriguing. Flower evolution is often
thought about from an adaptive perspective with the evo-
lution of floral form viewed as a function of reproductive
biology or pollination biology [2]. However, developmental
constraints and morphological potential can also be
viewed as a function of floral organogenesis, morphology,
and development rather than strictly an adaptive response
[3]. In recent years, studies of flower morphology in an
evolutionary and comparative context have been lifted by
genetic analyses of developmental pathways underlying
* Correspondence: wade.roberts@wsu.edu
1
Molecular Plant Sciences Graduate Program, Washington State University,
Pullman, WA 99164-1030, USA
2
School of Biological Sciences, Washington State University, Pullman, WA
99164-4236, USA
© The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Roberts and Roalson BMC Genomics (2017) 18:240
DOI 10.1186/s12864-017-3623-8
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

flower morphogenesis and biochemistry [4]. However,
understanding the macroevolutionary consequences of
flower modification through genetic and microevolution-
ary processes remains difficult. The difficulty arises from
the multitude of possible genetic changes available to
produce these phenotypic adaptations. Combining the
power of transcriptome sequencing with comparative
floral morphology allows for the exploration of the
possible evolutionary genetic mechanisms involved in
flower development and diversification.
We provide a first characterization of the floral tran-
scriptomes in four species of magic flowers, Achimenes.
This small genus of ~26 species is a member of the
African violet family (Gesneriaceae), a large family distrib-
uted in the New World and Old World tropics. The family
is renowned for its enormous diversity in habit, desicca-
tion tolerance, leaf morphology, and, particularly, floral
form [5–7]. Flower shape, color, and presentation are
hypothesized to be important for diversification and speci-
ation events in the family [7–11]. Convergence in floral
form is found across the family as well as in individual
genera and is likely tied to pollinator preferences and pol-
linator availability [7, 11]. In Achimenes, floral form
appears to be quite variable among closely related species
and similar corolla shapes and colors are found among
species that occur in different clades [10] (Fig. 1). Multiple
derivations of flower shape, color, and the presence of a
petal spur appear across the genus [10] (Fig. 1). Populations
of Achimenes are largely concentrated in central Mexico
south to Costa Rica, with some populations existing in the
Caribbean. General distributions of many closely related
species often overlap with many populations found growing
in the same habitat and elevation ranges [12]. Pollinator
studies have been limited with observations recorded for
only four species of Achimenes [13]. The major pol-
linator observed for each of the four Achimenes
species corresponds tightly with the hypothesized
pollination syndrome that was identified using combi-
nations of floral traits thought to be important for
pollinator attraction, such as color, shape, size, and orien-
tation of the open flower [10]. The young age of the genus
(~12 Ma) [7], coupled with a large number of shifts in
flower shape, color, and pollination syndrome [10], makes
Achimenes an ideal lineage to begin understanding the
ecological, evolutionary, and molecular forces contributing
to speciation and diversification of floral form.
Here, we present de novo floral transcriptome assem-
blies of four species of Neotropical Achimenes (Gesner-
iaceae) that vary in floral form, pigmentation patterns,
and pollination syndrome. Diversity of flower shape and
color among sister species in Achimenes present intri-
guing questions about the ecological and genetic forces
contributing to these phenotypic divergences. We
sampled flowers in three developmental stages from A.
cettoana, A. erecta, A. misera, and A. patens. This sam-
pling strategy allows inter- and intraspecies comparisons
of gene expression during development and comparisons
of sequence structure in order to begin investigating
evolutionary and developmental mechanisms contribut-
ing to speciation and diversification. Utilizing high-
throughput technologies has allowed researchers in both
animal [14, 15] and plant [16–18] systems to sequence
entire genomes, transcriptomes, and proteomes in order
to understand fine-scale patterns of genetics and evolu-
tion. Our study takes advantage of these genomic
approaches and provides resources that will serve as the
basis for future studies into flower development, evo-
lution, and plant-pollinator interactions.
Comparative transcriptomic studies in plants have seen
an increasing publication rate in recent years as sequen-
cing technologies keep increasing data output for lower
cost. Many studies have taken a focused look at compar-
ing developmental stages in a single species across
different tissues [19–21], comparing gene expression in
different organs [22, 23], or simply to generate preliminary
genomic data that will guide more detailed studies
[24–27]. Evolutionary questions have also been investigated
using genome-wide expression data in plants, such as the
evolution of gene expression patterns [16], parasitism [18],
self-fertilization [28], or mass flowering [29]. Our study
aims to bridge the gap between these different areas. We
took a developmental approach by sampling several stages
of flower development and an evolutionary approach by
comparing transcriptome data across multiple species. This
evolutionary-developmental approach to comparative tran-
scriptomics presents a novel way to investigate the patterns
and processes of flower diversification at the genomic level.
This study provides annotated reference transcriptomes for
four species of Achimenes and uses them for analyses of
sequence orthology, coexpression clustering of genes
during development, and selection tests to identify protein
sites undergoing positive selection. We also use data from
the transcriptomes to begin investigating the genetics of
flower color, particularly the production of anthocya-
nin pigments. It is our goal that the resources and
results provided herein will serve as the basis for
future studies. This study isamongthefirstexplora-
tions of Neotropical Gesneriaceae flower transcrip-
tomes using large-scale sequencing, and the results
described here may serve to guide further gene
expression and functional genomic studies in Achimenes
and other members of the Gesneriaceae.
Results
Assembly of high-quality achimenes floral transcriptomes
Sequencing the floral transcriptomes from three devel-
opmental stages in four species of Achimenes yielded
Roberts and Roalson BMC Genomics (2017) 18:240 Page 2 of 26
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

over 270 million reads (Table 1). Each species had
between 63 and 72 million paired-end reads se-
quenced (Table 1).
Trinity assemblies using a k-mer size of 25 produced
between 139,806 (A. cettoana) and 199,502 (A. erecta)con-
tigs for each reference transcriptome (Additional file 1).
Fig. 1 Phylogenetic relationships (modified from Roalson et al. [10]) and flower developmental stages sampled in Achimenes. aFloral
morphological characters of interest are mapped onto the tips of the cladogram, including: pollination syndrome, primary flower color, corolla
shape, and corolla gibbosity/presence of petal spurs. Four species were sampled for this study from across the genus and are indicated by a star.
bThe time-points sampled were: Bud, Stage D, and Pre-Anthesis flower buds. Bud stage was defined as pigmentation is largely absent and cells
are beginning to elongate. Stage D was defined as pigmentation beginning to accumulate and the corolla begins to elongate. Pre-Anthesis stage
was defined as flowers are nearly fully pigmented, the final size and shape of the flower has been determined, and the petal spur has developed
from the corolla tube (as in A. patens). Scale bar equals 1 cm. All photos provided by W.R.R
Roberts and Roalson BMC Genomics (2017) 18:240 Page 3 of 26
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

These assembled contigs had N50 values between 1444 (A.
misera) and 1794 (A. cettoana)bps,withmeanlengths
between 868 (A. misera) and 1027 (A. cettoana)bps
(Additional file 1). Velvet and Oases assemblies were
also performed using a range of k-mer sizes from 25 to 75
(Additional file 1). Generally, these assemblies produced
higher numbers of contigs, with higher N50 values, and
higher mean values than the Trinity assemblies
(Additional file 1). The number of contigs ranged from
46,189 in A. cettoana using a k-mer size of 75 to 247,516
in A. erecta using a k-mer size of 35 (Additional file 1).
N50 values were also showed some variation consistent
with larger k-mers producing lower values (1,385 in A.
misera) and smaller k-mers producing higher values
(2,334 in A. erecta; Additional file 1). Assemblies for A.
cettoana always produced far fewer contigs than the other
species (e.g., using Velvet/Oases, 126,317 in A. cettoana
versus 247,516 in A. erecta, see Additional file 1). The
number of contigs assembled does not appear to nega-
tively affect other assembly metrics; the mean length and
N50 values were similar across all species assemblies
(Additional file 1).
Merging the separate de novo assemblies reduced
redundancy and provided useful sets of contigs for fur-
ther analyses (Table 1; Additional file 1). Between 29,065
and 41,381 primary transcripts were obtained with N50
lengths between 1,990 and 2,113 bps (Table 1). The
merging process also provided between 23,332 and
105,442 alternate transcripts, which are composed of
possible isoforms (Table 1; Additional file 1).
Functional annotation and classification
The primary floral transcriptomes of A. cettoana, A. erecta,
A. misera,andA. patens were annotated by BLASTx
searches against the SwissProt [30] and the NCBI non-
redundant (Nr) protein database [31]. For A. cettoana,
18,364 (63.18%) sequences had hits in the SwissProt data-
base; A. erecta, 23,534 (56.87%) sequences had hits; A.
misera, 23,120 (56.00%) sequences had hits; and A. patens,
20,838 (54.98%) sequences had hits (Table 2). The numbers
of sequences with at least 75% coverage by their best
protein hits were 10,281 (35.37%), 12,372 (29.90%), 11,420
(27.66%), and 11,097 (29.28%), for each transcriptome
respectively. Against the Nr database, A. cettoana had
23,012 (79.17%) sequences with hits; A. erecta had 29,794
(72.00%) sequences with hits; A. misera had 29,783
(72.14%) sequences with hits; and A. patens had 26,776
(70.65%) sequences with hits (Table 2). Additionally, we
performed BLASTn searches against a collection of
Arabidopsis thaliana long non-coding RNA (lncRNA)
sequences acquired from the Plant Non-coding RNA Data-
base [32]. Against this set, A. cettoana had 76 (0.0026%)
sequences with hits; A. erecta had 96 (0.0023%) sequences
with hits; A. misera had 85 (0.0021%) sequences with hits;
and A. patens had 117 (0.0031%) sequences with hits
Table 1 Sequencing and summary statistics for Achimenes reference floral transcriptome assemblies and annotation
A. cettoana A. erecta A. misera A. patens
A. Sequencing
Total reads 67,428,998 63,582,836 69,588,964 71,960,488
Bud 21,112,016 18,680,312 24,016,214 22,585,994
Stage D 22,382,106 24,084,300 19,391,388 28,579,042
Pre-Anthesis 23,934,876 20,818,224 26,181,362 20,795,452
Total length (bp) 6,742,899,800 6,358,283,600 6,958,896,400 7,196,048,800
B. Final merged assembly
Primary transcripts 29,065 41,381 41,285 37,898
Alternate transcripts 23,332 94,172 105,442 65,115
N50 2,113 2,061 1,990 2,109
Mean length (bp) 1,417 1,268 1,260 1,304
Total bases, Primary set 41,202,771 52,511,722 52,038,201 49,447,956
Table 2 Overview of BLAST hits to primary transcript set and functional annotation output of the four reference transcriptomes
A. cettoana A. erecta A. misera A. patens
SwissProt 18,365 (63.18%) 23,534 (56.78%) 23,120 (56.00%) 20,838 (54.98%)
Nr 23,012 (79.17%) 29,794 (72.00%) 29,783 (72.14%) 26,776 (70.65%)
PNRD 76 (0.0026%) 96 (0.0023%) 85 (0.0021%) 117 (0.0031%)
GO 11,826 (40.69%) 14,996 (36.24%) 14,683 (35.56%) 13,179 (34.78%)
Abbreviations: GO gene ontology, Nr NCBI non-redundant protein database, PNRD plant non-coding RNA database
Roberts and Roalson BMC Genomics (2017) 18:240 Page 4 of 26
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

(Table 2). Non-coding ribosomal RNAs and tRNAs formed
a small number of the total contigs (Additional file 2).
The sequences with matches in the SwissProt [30] or
Nr [31] databases were further annotated with Gene
Ontology (GO) terms [33] based on the SwissProt data-
base, InterProScan [34], and ANNEX augmentation [35].
GO terms were assigned to 11,826 (40.69%) transcripts
in A. cettoana, 14,996 (36.24%) transcripts in A. erecta,
14,683 (35.56%) transcripts in A. misera, and 13,179
(34.78%) in A. patens (Table 2).Numbers and propor-
tions of sequences attributed to level 2 GO for Biological
Process (BP), Cellular Component (CC), and Molecular
Function (MF) type terms were qualitatively similar with
slight variations likely due to numbers of transcripts
assembled for each species (Additional file 3). Represen-
tation was qualitatively very similar between the four
species, with all level 2 GO categories exhibiting no
significant differences across species even after account-
ing for the effects of multiple testing (Χ
2
≥1.65, FDR-
corrected p-value ≥0:99, α= 0.001). We looked at further
GO levels (level 3, level 4, etc.) and found similar compos-
ition of category assignment for each transcriptome.
Core enzymes of the anthocyanin biosynthetic pathway
(ABP) were identified using HMMER [36] against homo-
logs downloaded from GenBank (Additional file 4). The
HMMER searches identified 224 proteins with similarity
to anthocyanidin synthase (ANS, Additional file 5), 122
proteins with similarity to dihydroflavonol 4-reductase
(DFR, Additional file 6), and 730 proteins with similarity
to both F3′H(flavonoid 3′-hydroxylase, Additional file 7)
and F3′5′H(flavonoid 3′,5′-hydroxylase, Additional file 7).
These large groups of proteins represent putative gene
families for each of these enzymes. Aligning the sequences
of these proteins with the sequences of known proteins
from other studies and constructing neighbor-joining trees
allowed us to identify putative proteins from Achimenes
involved in the ABP. We identified single copies of ANS
(Additional file 5), DFR (Additional file 6; Additional file 8),
F3′H(Additional file 7; Additional file 8), and F3′5′H
(Additional file 7; Additional file 8) in each transcriptome,
with the exception of A. misera where 6 copies of F3′5′H
were identified (Additional file 7). Five of the six A. misera
copies have very low normalized expression estimates and
may represent genes that are expressed at too low of level
to be detected at the current sequencing depth or may be
artifacts of our assembly process. Expression estimates for
each of the identified single copy enzymes generally
increases from B to A stages (Fig. 2) as pigments accumu-
late in the floral tissue.
Putative enzymes of the carotenoid biosynthetic
pathway (CBP) were identified from each Achimenes
transcriptome using BLASTx. Both bit scores and E-
values were used to identify best-hit transcripts. Using
homologs from Arabidopsis as query, there were 12
proteins identified to be involved in carotenoid biosyn-
thesis (Fig. 3). Proteins identified included ones belonging
to both the α-carotene and β-carotene branches (Fig. 3).
Proteins related to flower development were additionally
identified from each Achimenes transcriptome using
BLASTx. We used both bit scores and E-values to identify
putative proteins. Using homologs from Arabidopsis as
query, there were 101 putative proteins identified that
may be involved in flower development (Additional file 9).
Theseincludedproteinsinvolvedinfloweringtransition,
organ development, and floral repression (Additional file 9,
Additional file 10). Among the proteins identified were A-,
B-, C-, and E-class MADS-box genes, members of the
AP2/ERF family, numerous homeobox genes, and many
others (Additional file 9, Additional file 10). Each of these
proteins has a distinct expression domain during develop-
ment and may be expressed in floral organs (sepals,
petals,etc.),inthefloralmeristem,orintheinflores-
cence (Additional file 9, Additional file 10).
Several genes involved in cell proliferation and hor-
mone signaling were recently identified to be import-
ant for petal spur development in Aquilegia [37]. We
identified homologs of these genes from each tran-
scriptome using both bit scores and E-values to select
likely candidate transcripts. The Achimenes transcripts
identified include homologs of TCP4 and GIF1, both
involved in cell division control (Fig. 4). TCP4 dis-
tinctly shows very high expression in A. patens and
not the other Achimenes species, a similar pattern to
that observed in Aquilegia [37] (Fig. 4). Other genes
identified include STM involved in meristem indeter-
minacy [38], STY1 that regulates auxin biosynthesis
[39], ARF3 and ARF8 that are auxin response factors,
YUC6 and CYP71 both involved in auxin biosynthesis,
and DWARF4 and BEH4 that function in the brassi-
nosteroid pathway [40, 41] (Fig. 4).
Lastly, we identified candidate R2R3-Myb transcription
factors that may be involved in regulating anthocyanin and
carotenoid biosynthesis in flowers. Using HMM profiles
built from R2R3-Mybs shown to be involved in these path-
ways, we identified several candidate proteins. There are 8
Achimenes sequences identified that are closely related to
R2R3-Mybs from Erythranthe and Antirrhinum that regu-
lation floral anthocyanin production (Additional file 11).
Nine Achimenes sequences were identified and related to
an R2R3-Myb transcription factor in Erythranthe that
regulated floral carotenoid production (Additional file 11).
Core, shared, and unique genes
We found a set of gene clusters that were common to all
four Achimenes species and the outgroup Erythranthe
lewisii (collectively termed the “Core transcriptome”). This
core set of proteins consisted of 12,126 gene clusters (Fig. 5),
which comprised 59%, 48%, 50%, and 49% of the total
Roberts and Roalson BMC Genomics (2017) 18:240 Page 5 of 26
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

A
B
Fig. 2 Expression estimates for core enzymes of the anthocyanin biosynthetic pathway in Achimenes. LEGEND: a. Schematic outline of the core
anthocyanin biosynthetic pathway in plants. b. Expression (TPM) for the core enzymes of the anthocyanin biosynthetic pathway during flower
development in Achimenes. Expression for A. cettoana,A. erecta,A. misera, and A. patens are in blue, red, grey, and pink lines, respectively
Roberts and Roalson BMC Genomics (2017) 18:240 Page 6 of 26
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

A
B
Fig. 3 (See legend on next page.)
Roberts and Roalson BMC Genomics (2017) 18:240 Page 7 of 26
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

predicted proteins in A. cettoana, A. erecta, A. misera, and
A. patens, respectively (Fig. 5). There were an additional
1,776 gene clusters (Fig. 5) that were unique and shared
among all four gesneriad species (“Shared Achimenes”).
These clusters comprised 7.4%, 6.2%, 6.5%, and 6.7% of the
total predicted proteins, respectively (Fig. 5). In addition to
the shared clusters within Achimenes, each species also
contained unique protein sequences (unassigned to any
cluster) that were not found in any of the other five
transcriptomes; these unique sequences comprised 21–32%
of the transcriptomes (Fig. 5). Approximately 14% of the
transcriptomes were comprised of protein orthogroups
shared between at least two of the five species (“Shared
others”,Fig.5).
Among the 12,126 orthogroups that were shared by all
five species in the five-way comparison, there were 78
GO terms significantly enriched (FDR-corrected p-value
< 0.05). As expected, most of these terms were related to
primary metabolism, cellular components and structure,
signaling, reproduction, and response to stimulus, among
many others (Additional file 12). Within the protein
clusters that were shared among all four Achimenes species
(“Shared Achimenes”), 27 GO terms were significantly over-
represented (FDR-corrected p-value <0.05) in all species
(Additional file 13). When comparing protein sequences
that each species contributed to the “Shared Achimenes”
orthogroup, there were 7 overrepresented GO terms identi-
fied in all four species individually. Interestingly, each of
these terms were involved in DNA binding, including chro-
matin binding and transcription factor activity (Table 3).
Among the sequences that were unassigned to any clusters,
thereweresomedifferencesinthenumberandtypeofGO
terms that were significantly over- or underrepresented in
each species, with 4 terms identified in A. patens and 26
terms identified in A. erecta. (Additional file 14).
Quantifying expression and coexpression clustering
We estimated gene expression by mapping RNA-seq reads
from each developmental stage (B, Immature Bud; D,
Stage D; A, Pre-Anthesis) back to the respective reference
(See figure on previous page.)
Fig. 3 Expression estimates for core enzymes of the plant carotenoid biosynthetic pathway in Achimenes.aSchematic outline of the plant
carotenoid biosynthetic pathway. The enzymes are shown in boxes to the side of the arrows. Grey and orange boxes indicate the α-carotene and
β-carotene branches, respectively. bExpression (TPM) for the core enzymes of the carotenoid biosynthetic pathway during flower development
in Achimenes. Expression for A. cettoana,A. erecta,A. misera, and A. patens are in blue,red,grey, and pink lines, respectively
Ac Ac Ac Ae Ae Ae Am Am Am
TCP4−1
TCP4−2
GIF1
STM−1
STM−2
STY1−1
STY1−2
ARF3
ARF8−1
ARF8−2
ARF8−3
DWARF4−1
DWARF4−2
DWARF4−3
BEH4−1
BEH4−2
BEH4−3
YUC6
CYP71
Ap Ap Ap
expression
1
0.5
0
−0.5
−1
stage
bud
stageD
preA
Fig. 4 Expression of putative genes involved in petal spur development in Achimenes. Heatmap of scaled expression estimates for 10 Achimenes
homologs of Aquilegia genes hypothesized to be important for petal spur development according to [37]. Rows and columns are not clustered
Roberts and Roalson BMC Genomics (2017) 18:240 Page 8 of 26
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

‘primary’transcriptome using bowtie [42] and RSEM [43].
In each of the four species (A. cettoana, A. erecta, A. misera,
and A. patens), the mapping rate averaged 93.59%, 93.39%,
95.1%, and 92.07%, respectively. Additionally, mapping
reads from one species onto another species reference
produced successful mapping rates of >85%.
Over 5 independent runs, we used HTSCluster [44] and
the EM algorithm [45] to fit a sequence of Poisson
mixture models with K=1, 2, …, 60 clusters for the
expression estimates of each reference transcriptome.
Using slope heuristics (Djump, dimension jump; DDSE,
data driven slope estimation) [46], the number of clusters
was determined to be K= 34, 30, 29, 25 for the A.
cettoana, A. erecta, A. misera, and A. patens expression
estimates, respectively. Visualization of the clustering
displays numerous clusters with very high or very low ex-
pression levels during specific stages in development and
also many clusters where expression is not qualitatively
different between the three stages (Fig. 6; Additional file
15). Visualization of the maximum conditional probabil-
ities of cluster membership for each species indicates
confidence in cluster assignment (Additional file 16),
particularly among clusters that have distinct high or low
expression during a single developmental stage (Fig. 6;
Additional file 15, Additional file 16). Examining what, if
any, GO terms may be over- or underrepresented in
specific coexpression clusters may be useful to determine
any temporal patterns of gene expression during flower
development. In A. cettoana, 22 of 34 (65%) clusters had
significantly over-enriched GO terms associated. Likewise,
A. erecta had 23 of 30 (77%), A. misera had 21 of 29
(72%), and A. patens had 21 of 25 (84%) clusters with
significantly over-enriched GO terms (Additional file 17).
GO term enrichment tests were performed for each
cluster to identify general patterns of gene coexpression
(Additional file 18). Trends in gene coexpression were
apparent and what we expect for developing flowers. For
instance, genes involved in photosynthesis tended to have
higher expression in the B stage, while genes involved in
primary metabolism and biosynthetic processes tended to
be enriched in clusters without qualitative differences
between stages (Additional file 18). While considering
broad-scale patterns of gene categories that tend to be
coexpressed together provided important results, we add-
itionally wanted to investigate which clusters contained
genes involved in flower shape and pigment production.
Many members of the ABP were coexpressed together
(Additional file 19). In three species (A. cettoana,A. erecta,
and A. patens), several of the downstream enzymes were
found in the same coexpression cluster, including F3H,F3′
H,F3′5′H,DFR,andANS (Additional file 19). In A. misera,
all enzymes were put into different coexpression clusters
with the exception of CHI and F3′H(Additional file 19).
Several of the candidate R2R3-Mybs identified were also
coexpressed with enzymes of the ABP (Additional file 19).
One R2R3-Myb was coexpressed in A. cettoana with F3′5′
H;onewascoexpressedinA. misera with ANS;andone
was coexpressed in A. patens with F3H,F3′5′H,andANS
(Additional file 19).
There were very few enzymes of the CBP that were found
in the same coexpression cluster (Additional file 19). The
downstream enzymes of the β-carotene branch tended to
be found in the same coexpression cluster in some species,
particularly BCH,ZEP,NXS,andNCED (Additional file
19). Of the 9 candidate R2R3-Mybs identified, only one in
A. erecta was coexpressed with any of the CBP enzymes,
namely CYP97 (Additional file 19).
The genes identified to be involved in flower develop-
ment did not show any clear coexpression patterns. For
instance, genes that are involved in petal or carpel devel-
opment are found across many different clusters likely
due to very different temporal patterns of gene expres-
sion (Additional file 9, Additional file 10). Likewise, the
candidate genes we looked at for involvement in petal
spur development show very few coexpression patterns
(Additional file 19, Additional file 10). Some transcripts
of particular genes were coexpressed together, TCP4 in
A. patens for example, while most others were found in
different coexpression clusters (Additional file 19).
Detecting proteins under selection
As detection of positive selection requires a minimum of
five species to obtain reliable estimates [47], orthogroups
from the five-way analysis were stringently filtered. These
filtering steps provided 2,930 orthogroups, containing
26,141 total sequences, for selection analyses. Sequence
alignments were visually inspected to identify spurious
alignments that could produce false positives in our selec-
tion analyses. After inspection, no clusters were removed
from the subsequent analyses. Likelihood ratio tests com-
paring four models (M1a vs. M2a, M7 vs. M8) [48, 49]
were employed to identify proteins and amino acids
within those proteins potentially displaying signatures of
selection. Comparison of M1a versus M2a (m12) identi-
fied 339 orthogroups containing proteins with signatures
of selection, while M7 versus M8 comparisons (m78)
identified 642 orthogroups (FDR-corrected p-values
≤0.05). Three hundred thirty-five orthogroups were iden-
tified by both m12 and m78 comparisons. The numbers of
proteins identified in m12 were 64, 80, 68, and 76 for A.
cettoana, A. erecta, A. misera, and A. patens, respectively
(Additional file 20). In the m78 comparison, there were
125, 144, 133, and 143 proteins identified, respectively
(Additional file 21).
Enrichment tests did not show any GO terms signifi-
cantly over- or underrepresented in the list of proteins
with sites undergoing positive selection. Comparisons
were made both for the combined set of proteins, as well
Roberts and Roalson BMC Genomics (2017) 18:240 Page 9 of 26
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Core
Shared Achimenes
Shared others
Unique
0
10000
20000
30000
40000
AC AE AM AP
Number of proteins
Orthogroup composition
Annotated Alternate
Annotated Primary
Unannotated
0.00
0.25
0.50
0.75
1.00
AC AE AM AP
Percentage
Protein annotation
Unassigned
AC: 6,075 598
435
379
213
304
282
1,776
142
304
298
12,126
97
227
108
202
Unassigned
AE: 13,334
Unassigned
AM: 12,288
Unassigned
AP: 11,699
Unassigned
EL: 34,537
1,068
716
463
275
415
153
166
302
360 185
793
AC: 17,214
AE: 19,960
AM: 20,903
AP: 18,768
EL: 41,698
AC: 2,162
AE: 2,603
AM: 2,697
AP: 2,584
Achimenes erecta (AE)
Achimenes
misera (AM)
Achimenes
patens (AP)
Erythranthe
lewisii (EL)
Achimenes
cettoana (AC)
AE: 543
AM: 577
AP: 583
EL: 606
AE: 588
AM: 588
AP: 590
AM: 220
AP: 236
EL: 261
AC: 256
AM: 300
AP: 284
EL: 322
AC: 349
AE: 376
AP: 374
EL: 447
AC: 327
AE: 354
AM: 368
EL: 386
AM: 836
AP: 870
AE: 1128
AM: 1120
AC: 446
AM: 466
AC: 334
AE: 393
AM: 373
AC: 237
AM: 265
AP: 278
AC: 124
AP: 128
EL: 114
AC: 118
AM: 119
EL: 159
AC: 225
EL: 251
AC: 392
AP: 401
AC: 352
AE: 380
AP: 358
AC: 157
AE: 165
EL: 201
AE: 171
AP: 185
EL: 211
AE: 760
AP: 773
AP: 317
EL: 424
AC: 608
AE: 636
AE: 302
EL: 341
AE: 196
AM: 199
EL: 229
AM: 408
EL: 411
Core
Shared Achimenes
BC
A
Fig. 5 Sequence orthology and the proportion of annotated sequences in the Achimenes transcriptomes. aVenn diagram showing the number
of shared or unique genes (in italics) and gene clusters (bold) among the five Achimenes species and Erythranthe as classified by the OrthoFinder
program. “Core”and “Shared Achimenes”orthogroups are indicated with blue and red stars, respectively. Abbreviations: AC, Achimenes cettoana;
AE, Achimenes erecta; AM, Achimenes misera; AP, Achimenes patens; EL, Erythranthe lewisii.bProportion of the transcripts that comprised core,
shared Achimenes, shared others, and unique genes. “Core”orthogroups were common to all four Achimenes and Erythranthe. “Shared Achimenes”
are orthogroups that contain sequences from all four Achimenes species. “Shared other”are orthogroups present in two or three of the four
Achimenes species. “Unique”genes are genes that are only present in one species and were unassigned to a specific orthogroup. cProportion of
annotated and non-annotated genes in the primary and alternate transcriptomes
Roberts and Roalson BMC Genomics (2017) 18:240 Page 10 of 26
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

as protein sets for each individual species (including
Erythranthe). However, several GO categories of interest to
the current study were found in the protein set, including
terms including flower development, anatomical structure
morphogenesis, anthocyanin pigmentation, and transcrip-
tion factor activity (Table 4).
Discussion
This study is among the first to employ RNA sequencing
for comparative studies both between species and between
developmental stages in flowering plants [16, 18]. This
study is also among the first to characterize and annotate
floral transcriptomes in Neotropical Gesneriaceae, a
lineage well known for diverse and colorful flowers [50].
Achimenes offers a unique opportunity to study the gen-
omics of flower diversification in a comparative context
because very closely related species display an extraordin-
ary range of morphological diversity likely tied to pollin-
ator preferences and shifting patterns of gene expression.
Rather than using a candidate-gene approach to under-
stand patterns of speciation and diversification, we utilize
high-throughput sequencing to begin searching for the
potential pathways involved. We assembled between
29,000 and 42,000 putatively unique primary and alternate
transcripts for four species of Achimenes that display
many of the most common floral forms found in the
genus. Orthogroup detection among Achimenes and
against an Erythranthe corolla transcriptome revealed
numerous conserved and distinct transcript clusters
expressed among species (Fig. 5). Coexpression clustering
revealed distinct patterns of gene expression in different
stages of development (Fig. 6; Additional file 15).
Table 3 Gene ontology terms overrepresented in the “Shared Achimenes”orthogroups
Term Description Type FDR Single-test p-value Number in
test group
Number in
reference group
GO:0003676 Nucleic acid binding MF 4.20e-46 2.30e-48 977 7373
GO:0003677 DNA binding MF 2.10e-44 2.30e-46 612 3951
GO:0003682 Chromatin binding MF 3.10e-28 6.80e-30 157 619
GO:0044877 Macromolecular complex binding MF 3.10e-28 6.80e-30 157 619
GO:0003700 Transcription factor activity, sequence-specific DNA
binding
MF 6.30e-20 2.00e-21 200 1112
GO:0001071 Nucleic acid binding transcription factor activity MF 6.30e-20 2.00e-21 200 1112
GO:1901363 Heterocyclic compound binding MF 2.70e-08 1.20e-09 1517 16173
GO:0097159 Organic cyclic compound binding MF 2.70e-08 1.20e-09 1517 16173
GO:0005618 Cell wall CC 3.10e-06 1.50e-07 43 199
GO:0030312 External encapsulating structure CC 4.50e-06 2.40e-07 43 203
GO:0005488 Binding MF 1.30e-04 8.00e-06 2737 31646
GO:0090304 Nucleic acid metabolic process BP 2.70e-04 1.90e-05 145 1210
GO:0006259 DNA metabolic process BP 2.70e-04 1.90e-05 145 1210
GO:0030246 Carbohydrate binding MF 2.10e-03 1.60e-04 63 456
GO:0019825 Oxygen binding MF 5.60e-03 4.60e-04 5 5
GO:0071944 Cell periphery CC 5.60e-03 4.90e-04 58 430
GO:0009653 Anatomical structure morphogenesis BP 7.00e-03 6.50e-04 7 14
GO:0015979 Photosynthesis BP 9.40e-03 9.20e-04 35 229
GO:0006725 Cellular aromatic compound metabolic process BP 1.90e-02 2.30e-03 590 6399
GO:1901360 Organic cyclic compound metabolic process BP 1.90e-02 2.30e-03 590 6399
GO:0046483 Heterocycle metabolic process BP 1.90e-02 2.30e-03 590 6399
GO:0006139 Nucleobase-containing compound metabolic
process
BP 1.90e-02 2.30e-03 590 6399
GO:0005576 Extracellular region CC 2.10e-02 2.60e-03 31 209
GO:0005634 Nucleus CC 2.80e-02 3.70e-03 236 2391
GO:0009607 Response to biotic stimulus BP 3.80e-02 5.20e-03 19 113
GO:0005615 Extracellular space CC 4.20e-02 6.20e-03 6 17
GO:0044421 Extracellular region part CC 4.20e-02 6.20e-03 6 17
Abbreviations: BP biological process, CC cellular component, FDR false discovery rate corrected p-value, MF molecular function
Roberts and Roalson BMC Genomics (2017) 18:240 Page 11 of 26
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Assessing protein sequences for signatures of positive selec-
tion revealed numerous protein sites under selection in
proteins involved in flower development, pollination, and
transcription factor activity (Table 4, Additional file 20,
Additional file 21). To further explore each of these analyt-
ical approaches, we annotated all transcriptomes with gene
ontology terms for quantitative comparison. The overall
GO representation of each transcriptome qualitatively
matches that of other floral transcriptomes [28, 51] and
there were no significant deviations in the GO representa-
tions between the four species of Achimenes. Comparisons
of expression patterns for genes involved in anthocyanin
and carotenoid biosynthesis, as well as flower development,
also allow for further understanding of the temporal and
evolutionary patterns of the expressed genes.
Assembly and consensus transcriptome
Experiments that use transcriptome sequencing have
several considerations, including how many replicates to
sequence and how much sequencing to perform. The
aims of our study were to generate preliminary tran-
scriptome data for four species with three developmental
time points in each. Our experiment produced
sequenced between 6.3 and 7.2 Gb pairs for each species
we sampled (Table 1). From recent transcriptome ana-
lyses in other non-model plants, the read generation per
sample is commonly 2 to 5 Gb [24, 52–54]. By combin-
ing the time point samples in each species, we hoped to
provide a large set of reads for de novo reference assembly.
After combining reads for each time point, the average
number of base pairs used for assembly was 6.8 billion
(Table 1), similar to these other studies [24, 52–54]. We
believe this provides us with an adequate number of reads
for initial characterization of our non-model plant
subjects. As would be expected, increasing the sequencing
depth for a given sample will greatly improve the ability to
identify novel and unique transcripts. Future experiments
in Achimenes will add additional sequencing depth and
Fig. 6 Coexpression cluster profiles of Achimenes cettoana transcripts using Poisson mixture models. Thirty-four coexpression clusters were determined for
A. cettoana with Poisson mixture models using slope heuristics as implemented in [44]. Clusters 1 to 18 are presented here to provide an example of the
dynamic patterns of gene coexpression seen during flower development in A. cettoana. The full figure showing all 34 clusters is included in Additional
file 15. Boxplots indicate average gene expression profiles for each cluster. Conditions refer to the sampled stages of flower development: 1, Bud stage;
2, Stage D; and 3, Pre-Anthesis stage
Roberts and Roalson BMC Genomics (2017) 18:240 Page 12 of 26
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Table 4 Genes under positive selection in Achimenes and Erythranthe related to flower development and pigmentation
Gene Description UniProd ID Species Test
5MAT1 Malonyl-coenzyme:anthocyanin 5-O-glucoside-6”’
-O-malonyltransferase
Q8W1W9 Ac m12,m78
SFH13 Phosphatidylinositol/phosphatidylcholine transfer
protein SFH13
Q501H5 Ac m78
UBC28 Ubiquitin-conjugating enzyme E2 28 Q94F47 Ac m78
Y5241 Probable receptor-like protein kinase at5g24010 Q9FLW0 Ac m12
LECRK91 L-type lectin-domain containing receptor kinase Q9LXA5 Ac m12
GAUT14 Galactouronosyltransferase 14 Q8GWT1 Ac m12
ABCB19 ABC transporter B family member 19 Q9LJX0 Ae m12
ACR4 ACT domain-containing protein ACR4 Q8LJW3 Ae m12
CYP90A1 Cytochrome P450 90A1 Q42569 Ae m12
DCR BAHD acyltransferase DCR Q9FF86 Ae m12
DFRA Dihydroflavonol 4-reductase P51102 Ae m78
MAP70.2 Microtubule-associated protein 70-2 Q8L7S4 Ae m12
TA14B Transcription initiation factor TFIID subunit 14b Q9FH40 Ae m12
TKPR1 Tetraketide alpha-pyrone reductase 1 Q500U8 Ae m12
GPPL2 Haloacid dehalogenase-like hydrolase domain-
containing protein at3g48420
Q94K71 Ae m12
HAT Zinc finger bed domain-containing protein
DAYSLEEPER
Q9M2N5 Ae m12
KDSB 3-deoxy-manno-octulosonate Q9C920 Ae m12
BIG1 Brefeldin A-inhibited guanine nucleotide-exchange
protein 1
FAJSZ5 Ae m12
PRMT13 Probable histone-arginine methyltransferase 1.3 Q84W92 Am m12
ATX1 Copper transport protein ATX1 Q94BT9 Am m12
CYP71A1 Cytochrome P450 71A1 P24465 Am m12
FLXL1 Protein FLX-like 1 Q93V84 Am m12,m78
FRI Protein FRIGIDA P0DH90 Am m12,m78
DTX41 Protein DETOXIFICATION 41 Q9LYT3 Am m12
Y1301 BTB/POZ domain-containing protein at1g03010 Q9SA69 Am m12
AKR2A Ankyrin repeat domain-containing protein 2A Q9SAR5 Ap m78
CAF2M CRS2-associated factor 2 Q9FFU1 Ap m12
CKB2 Casein kinase II subunit beta-2 P40229 Ap m12
GN ARF guanine-nucleotide exchange factor GNOM Q42510 Ap m12
WNK1 Serine/threonine-protein kinase WNK1 Q9CAV6 Ap m12
HDA19 Histone deactylase 19 O22446 Ap m12
PRXQ Peroxiredoxin chloroplastic Q6UBI3 Ap m12
CPK13 Calcium-dependent protein kinase 13 Q8W4I7 Ap m12
LOL2 Protein LOL2 O65426 Ap m12
EXO70A1 Exocyst complex component EXO70A1 Q9LZD3 Ap m12
PRMT11 Protein arginine N-methyltransferase 1.1 Q9SU94 El m12
BLH8 BEL1-like homeodomain protein 8 Q9SJJ3 El m12
GDL15 GDSL esterase/lipase at1g29670 Q9C7N4 El m12
GK-2 Guanylate kinase 2 Q9M682 El m12
CUT1 3-ketoacyl-CoA synthase 6 Q9XF43 El m12
AATL1 Lysine histidine transporter-like 8 Q9SX98 El m12,m78
Roberts and Roalson BMC Genomics (2017) 18:240 Page 13 of 26
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

replicates. We additionally attempted to assemble the best
set of transcripts with our data in order to perform
comparative analyses relevant to floral developmental pro-
cesses. Our approach to do numerous assemblies using
different parameter settings was an attempt to generate as
many complete transcripts as possible. Quality of our as-
semblies was confirmed by sequence comparison through
orthology-based analyses and annotation of transcripts to
known genes from model plant species. BLASTx hits to
SwissProt proteins that had >80% coverage constituted
between 34% and 40% of our assembled transcripts. These
factors provide confidence that our experimental
approach was able to meet the aims of our study and to
provide initial characterization of the floral transcriptomes
in non-model plants.
Our study is among the few that use a multiple assem-
bler approach [55–57]. Rather than relying on a single
de novo assembly program for all contig assembly, we
used a combination of Trinity, Velvet, and Oases, to
create seven assemblies for each transcriptome that we
then merged into a single reference set of contigs. This
approach has been used by other studies with success in
increases contig length, recovering more unique tran-
scripts, and minimizing sequence redundancy [55–57].
Our approach additionally took advantage of multiple
k-mer lengths for assembly in Velvet and Oases.
Multiple k-mer sizes have been demonstrated to assem-
ble more lowly and highly expressed full-length transcripts
than using a single k-mer size alone [58]. Our Trinity
assemblies produced fewer contigs with lower N50 and
mean lengths than the Velvet/Oases assemblies (Additional
file 1). As the k-mer size increases, from 25 to 75, the
Velvet and Oases assemblies produced fewer contigs with
lower N50 and mean lengths (Additional file 1). Larger
k-mer sizes also appeared to assemble the largest contigs
even though the mean length overall was lower.
Although summaries of the distribution of contig
lengths are informative, the goal of transcriptome assem-
bly is not longer sequences, but rather accurate se-
quences. One metric that remains informative is the
proportion of contigs that have significant similarities to
known proteins. The difficulty in this measure stems
from studies reporting slightly different results using
different BLAST parameters and databases. However,
nearly 80% of our combined assembly of primary and
alternate transcripts had matches in SwissProt or Nr and
this value is as high or higher than all other comparable
statistics reported in other de novo assemblies [20, 24, 28].
Another useful metric is the proportion of the contig and
its corresponding best BLAST hit that align to one
another. Between 11,420 (27.66%) and 10,281 (35.37%)
contigs are covered by at least 75% of their best BLAST
hit. These results provide strong evidence that the contigs
we assembled in absence of a reference genome largely
represent real transcripts and not assembly error.
Core, shared, and unique genes
Our results indicate that the four Achimenes species in
our study share a core set of genes expressed during
flower development that may also be more broadly
shared among other gesneriads. These transcripts code
for proteins involved in essential cellular and metabolic
functions, such as glycolysis, photosynthesis, and amino
acid metabolism (Additional file 12). The transcriptomes
also contained “shared”genes, which were observed in
two or three of the four target species. There is limited
data on how much physiological diversity might be
present among such closely related gesneriad species
because these taxa have been traditionally defined based
on morphological features alone [10, 12]. Therefore, we
were interested in what our data may reveal about the
relatedness of these closely related taxa. Within the cluster
that was unique to all four species (“Shared Achimenes”),
there was significant overrepresentation of proteins in-
volved in DNA binding and transcription factor activity
(Table 3, Additional file 13). This may represent an artifact
of our orthogroup clustering approach because our chosen
comparison (Erythanthe) was a corolla-specific transcrip-
tome rather than whole developing flower as in our
samples. We expect that our sampling would capture
additional transcripts representing transcription factors
involved in calyx, stamen, and ovule development that
may be missing from the Erythranthe transcriptome. The
Erythranthe transcriptome is from corolla tissue; therefore,
Table 4 Genes under positive selection in Achimenes and Erythranthe related to flower development and pigmentation (Continued)
MAA3 Probable helicase MAGATAMA 3 B6SFA4 El m12,m78
RH27 DEAD-box ATP-dependent RNA helicase 27 Q9SB89 El m12
SOBIR1 Leucine-rich repeat receptor-like serine/threonine/
tyrosine-protein kinase SOBIR1
Q9SKB2 El m12
BRM ATP-dependent helicase BRM Q6EVK6 El m12
AGO2 Protein argonaute 2 Q9SHF3 El m12
ARID5 AT-rich interactive domain-containing protein 5 Q0WNR6 El m12
Abbreviations: Ac Achimenes cettoana,Ae Achimenes erecta,Am Achimenes misera,Ap Achimenes patens,El Erythranthe lewisii,m12 PAML model comparison m1a vs.
m2a, m78 PAML model comparison m7 vs. m8
Roberts and Roalson BMC Genomics (2017) 18:240 Page 14 of 26
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

a more complete sampling of the flower would provide a
more complete comparison. The overrepresentation of
DNA binding activity may also represent an expansion
and specialization of transcription factor gene families in
Achimenes thatmayhavearoleindeterminingmanyof
the unique phenotypes seen. Additional sampling of whole
flowers in related species may provide insight into these
two possibilities. The remainder of transcripts (approxi-
mately one quarter) in each of our four Achimenes
transcriptomes was found in a single species (Fig. 5). The
numbers of transcripts that were putatively species-
specific is higher than what we would expect given the
close phylogenetic relationships of the four species.
Enrichment analyses also did not indicate large numbers
of GO terms over- and underrepresented in each species
(Additional file 14). Even with the large number of these
unassigned transcripts, our assembly pipeline reduced
nearly all redundancy by removing identical and closely
related sequences.
Coexpression clustering
Coexpression clustering allows us to identify biological
entities (e.g., genes) that share similar profiles across several
developmental stages and may help identify groups of genes
that are involved in the same biological processes [59, 60].
While we are unable to perform standard analyses of differ-
ential expression in the current study (no biological repli-
cates), coexpression clustering provides interesting and
useful information on the dynamic temporal changes in
gene expression that occur during flower development.
Future studies will include additional replicates to perform
statistical analyses of differential expression both within
and between species of Achimenes. Clustering analyses
based on metric criteria, such as k-means [61] or hierarch-
ical clustering [62], have been broadly used to cluster
microarray-based measures of gene expression, as they are
rapid, simple, and stable. These approaches require the user
to decide on the metric and criterion to be optimized, as
well as selecting the appropriate number of clusters, which
may not be biologically relevant [63]. We chose an alterna-
tive approach, namely probabilistic clustering that uses
Poisson mixture models that allowed us a straightforward
approach for parameter estimation and model selection for
cluster assignment, as well as a per-gene conditional prob-
ability of belonging to each cluster. Other model based
clustering approaches may also utilize negative binomial
(NB) algorithms (such as MBCluster.Seq) [64]. Poisson
models have been shown to fit well to data without
biological replicates [65] and NB models to data with
biological replicates [66]. We therefore use Poisson models
to explore patterns of coexpression in our transcriptomes.
Clustering selected between 25 and 34 groups for our
transcriptomes that represented genes with shared expres-
sion profiles (Fig. 6; Additional file 15; Additional file 17).
Enrichment tests validated our approach by identifying
significant GO terms that were overrepresented in numer-
ous clusters. A majority of clusters in each species had
overrepresented GO terms (Additional file 17, Additional
file 18). This clustering approach provides us with groups
of genes that are expressed in similar stages that may be
linked with particular metabolic or biosynthetic pathways
of interest. Coexpression clustering has often been
combined in other systems with experimental data or
metabolic profiling [67, 68]. Combining clustering data
with other approaches has the ability to provide additional
support for specific patterns or processes detected from
clustering. Obtaining lists of GO terms enriched in coex-
pression clusters is another useful approach to find
patterns within large datasets that can then be used to
guide experimental approaches to validate and provide
additional support for the patterns seen. Our approach to
coexpression clustering differs from commonly used
coexpression network approaches that also seek to find
biologically interesting clusters of genes sharing similar
functional roles. Network analyses, which often use the
Weighted Gene Correlation Network Analysis method
(WGCNA) [69], usually require at least 15 samples to
produce reliable results. Network approaches have been
used in other floral transcriptomes to uncover gene
networks involved in developing organs [70], floral bud
development [71], and pistillate flowering [72]. In future
analyses of Achimenes, additional replicates and sampling
will allow us to perform network-based analyses that may
uncover additional gene network modules involved in
flower diversification.
Flower development: spurs
Numerous molecular genetic studies have demonstrated
the crucial role of transcription factors in reproductive
development of plants. The homologs of many of the genes
identified in our study are well known to regulate aspects
of flower development in model systems, such as Arabidopsis.
As expected, we observed an abundance of genes involved
in various processes related to flower development, such as
the transition to flowering and floral organ identity
(Additional file 10). Clear patterns are apparent for genes
showing high or low levels of expression during the differ-
ent developmental time points we sampled. Many studies
that have used transcriptome sequencing to understand
flower development have focused on sequencing individual
floral organs (e.g., petals, stamens, etc.) and comparing
them to identify genes differentially expressed between
organs [22, 25, 54, 73]. Comparing expression between
different tissues has the advantage of being able to identify
where individual genes show high or low expression levels.
Often these studies focus on a single species. Our aims for
the current study were instead to investigate and compare
the floral transcriptome in many closely related species that
Roberts and Roalson BMC Genomics (2017) 18:240 Page 15 of 26
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

exhibit very diverse flowers. The advantage of our approach
is the ability to begin understanding how gene expression
differences may contribute to phenotypic differences
among closely related species. We identified over 100 tran-
scripts likely involved in flower developmental processes
(Additional file 10). These transcripts in Achimenes largely
show similar expression patterns seen in other flowering
plants [22, 25, 74]. The orthologs of many well-known
MADS-box genes (e.g., AP1, AP3, PI, and AG)arecrucial
for orchestrating floral organ identify [75, 76]. The expres-
sion patterns of these genes follow what we might expect
given when the different floral organs are developing in
Achimenes flowers (Additional file 10). For instance, the A-
class genes AP1 shows high expression during the bud
stage when sepals are developing and the B-class genes
AP3 and PI have increased expression during D stage when
petals are developing (Additional file 10). Elaboration of
the petals to produce different shapes and widths likely
involves genes outside these MADS-box genes [77].
Some species of Achimenes (including A. patens)exhibit
a unique spur-like outgrowth of the petal tube that
extends in opposition to the tube opening (Fig. 1). This
petal spur has evolved independently at least three times
in Achimenes, mostly in butterfly-pollinated species where
the flower is presented at a downward angle (Fig. 1). The
purpose of this petal spur in Achimenes has yet to be
elucidated; it differs from the spurs in other lineages (such
as columbines, Aquilegia) by not containing nectary tissue
[10]. The genetic factors influencing the development of
spurs have not yet been fully understood. Recent transcrip-
tome sequencing of developing spur tissue in Aquilegia
identified several candidate genes for this process, including
homologs of TCP4, GRF1, and many other genes that
contribute to cell proliferation and auxin signaling [37]. We
seeanincreasedlevelofgeneexpressionforTCP4 in A.
patens in the stages where spur growth is seen while this
gene in the other three species remains much lower (Fig. 4).
WealsoseeanincreaseingeneexpressionofST Y1 and
ARF8 in A. patens, similar to what was reported in Aquilegia
(Fig. 4). With the patterns seen in A. patens relative to the
other species, we can hypothesize that TCP4 may be
playing a significant role in the development of the petal
spur. KNOX genes, particularly STM,havealsobeen
hypothesized to be important players in petal spur devel-
opment in Antirrhinum and Linaria [78, 79]. Overexpres-
sion of KNOX genes in Antirrhinum produced spur-like
outgrowths in the floral tube [78], while KNOX genes in
Linaria displayed increased expression in petal spur tissue
[79]. Our expression estimates for STM across Achimenes
do not offer as clear a pattern as TCP4;STM gene expres-
sion patterns are similar across several species (Fig. 4).
The pattern of STM expression is similar in both A. patens
and A. misera (Fig. 4). Testing the functional roles of
TCP4 and STM will be important in future work to
determining which is more likely to be important for petal
spur growth in Achimenes.
Flower color: anthocyanins
Differences in flower color are one of the most distinguish-
ing characters that separate Achimenes species. Flowers
across the genus display an amazing array of colors and
color patterns, including species with white, yellow, red,
blue, and purple pigmentation [10, 12] (Fig. 1). The primary
pigment in flowers of Achimenes and most angiosperms are
anthocyanins, a class of flavonoids that represent a large
group of secondary metabolites [80]. The types of pigments
present in floral tissue vary across Achimenes species, with
all taxa containing anthocyanins and several containing a
mix of anthocyanins and carotenoids. Anthocyanins con-
tribute hues of blues, purples, and reds due primarily to
production of pelargonidins, cyanidins, and delphinidins
[80]. In plants, the biochemistry of the ABP is very well
studied and understood in both model systems (e.g.,
Arabidopsis) [81] and non-model systems (e.g., Aquilegia,
Mimulus, and Iochroma)[82–86]. While the biochemical
reactions involved in the ABP are well understood, further
research aims at understanding how the genetics of the
pathway contributes to species differences in pigment
production and the role it plays in adaptive evolution. The
ABP is composed of 7 structural loci, with many of the
earliest steps highly conserved in plants due to their role
in producing precursor products involved in defense and
UV protection [80, 81] (Fig. 2). The downstream pathway
splits into 3 branches that lead to production of red
pelargonidins, purple cyanidins, and blue delphinidins
[80]. Flux down any of these branches is largely deter-
mined by the activity of two enzymes: F3′Hand F3′5′H.
Downregulation or inactivation of these enzymes can
cause flux to be redirected down a different branch, result-
ing in a different flower color.
Several possible routes to produce variation in anthocya-
ninproductionexist,includinggene loss or transcriptional
regulation. One predominant example seen numerous
times across flowering plants is the shift from blue-colored
flowers to red-colored flowers that is closely associated with
a shift from bee pollination to bird pollination [84, 85, 87–90].
These studies have implicated the downstream enzymes
of the ABP (particularly ANS,DFR,F3′H,andF3′5′H)
being involved in flower color transitions. Primarily, two
often predictable routes have been suggested for the tran-
sition from blue to red anthocyanin pigment production:
acquisition of mutations in DFR that alter its substrate spe-
cificity [84–86] or altered expression of F3′Hand F3′5′H
resulting from cis- of trans-regulatory mutations [84, 88–90].
Given the constrained structure of the ABP and the few
demonstrated genetic changes involved in flower color
transitions, our focus in Achimenes lays in genetic changes
involving the enzymes DFR,F3′H,andF3′5′H,aswellas
Roberts and Roalson BMC Genomics (2017) 18:240 Page 16 of 26
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

the R2R3-Myb transcription factors that regulate the ABP
[86, 91].
In Achimenes, multiple transitions from blue to red exist
[10], and there also exists at least one likely red-to-blue
flower color transition on the branch leading to A. cettoana
(Fig. 1). This type of transition is exceedingly rare in plants
and has few documented explanations. The transition of
blue-to-red is more common and often involves predict-
able changes to key enzymes of the ABP, including DFR,
F3′H,andF3′5′H(see Discussion above). One such case
of red-to-blue flower color transition involves a gene
duplication of F3′Hand neofunctionalization to regain the
role of F3′5′Hin Asteraceae [92, 93]. A similar gene dupli-
cation event is not found when the gene trees are examined
for F3′Hand F3′5′H(Additional file 7), suggesting that
changes in gene expression are more likely involved in a
red-to-blue color transition in Achimenes.
We captured transcripts of core downstream enzymes of
the ABP from all 4 transcriptomes, each with appreciable
expression levels that show an increase from B to A stage
(Fig. 2). Several patterns of expression emerge from the
data. Both A. cettoana and A. patens have increased expres-
sion of F3′5′H, the enzyme responsible for directing the
flux of the pathway toward delphinidin production (Fig. 2).
These flowers are blue and purple, so this pattern is what
we might expect to see. Expression levels of the enzymes in
A. misera are much lower, which we might also expect
given that this flower produces very little pigment except in
areas of the corolla throat (Fig. 2). Expression of F3′5′His
much lower in A. erecta, the red-flowered species (Table 3)
and this pattern follows the pattern seen in other systems
[83, 85]. The possible explanation for how the red-to-blue
color transition could have occurred in Achimenes will
require more detailed studies than those presented here,
but given that we see expression of all ABP enzymes, it is
possible that differences in anthocyanin production are due
to genetic changes in the transcription factors that regulate
the pathway, not in loss of function mutations as found in
other systems [83–85, 94]. Additionally, Achimenes species
tend to produce anthocyanins in both floral and vegetative
tissue [12]. This coupled with the captured expression of
the ABP enzymes may suggest that flower color transitions
may involve a change to substrate specificity in DFR or in
the downregulation of F3′Hand F3′5′Henzymes in red
flowers through trans-activating mutations.
It is interesting to find that several of the ABP enzymes
are coexpressed together and in three species (A. cettoana,
A. misera,andA. patens) they are coexpressed with candi-
date R2R3-Mybs that we identified (Additional file 19). In
A. cettoana, the candidate R2R3-Myb is coexpressed with
F3′5′H, the enzyme that directs the metabolic flux of the
pathway toward the production of delphinidins (Fig. 2).
Another candidate R2R3-Myb in A. patens was coex-
pressed with F3H,F3′5′H,andANS (Additional file 19).
With this pattern in these two species, we might
hypothesize that the candidate R2R3-Mybs are involved in
transcriptional regulation of the ABP to produce delphini-
din pigments. This is what we would expect given the blue
and purple flower color in these species. In A. misera,one
candidate R2R3-Myb was coexpressed with ANS and
might be involved in regulating more downstream parts of
the ABP (Additional file 19).
The role of R2R3-Myb transcription factors in regulat-
ing various steps of the ABP has been well studied in
numerous plants [86, 91, 95] and the possible role of these
transcription factors in Achimenes will need to be studied
further. We identified putative proteins in Achimenes with
high-similarity to R2R3-Mybs that have experimental
evidence indicating their role in regulating anthocyanin
accumulation (Additional file 11). These Achimenes R2R3-
Mybs are closely related to homologs recently identified in
Erythranthe [86] as well as homologs from Petunia [96]
and Antirrhinum [97]. We can hypothesize that these
R2R3-Mybs from Achimenes may function similarly to
regulate expression of the ABP given their close similarity
to other homologs as well as their coexpression patterns.
Flower color: carotenoids
Carotenoids are important pigments that carry out func-
tions in protecting the photosynthetic apparatus from
photooxidative damage and acting as accessory pigments in
light harvesting [98]. In non-photosynthetic tissues, carot-
enoids are usually synthesized as secondary metabolites
and accumulate in chromoplasts, providing the yellow,
orange, and red colors in many flowers, thus serving an
important function in the ecology and evolution of plants
by attracting pollinators and seed dispersers [99]. In many
Achimenes species, carotenoids are found throughout the
corolla; while in other species carotenoid production is
limited to the corolla throat (as in A. erecta and A. misera).
Few species, including A. cettoana and A. patens, do not
appear to produce carotenoids in the corolla tissue and only
produce anthocyanins.
We identified putative enzymes in the plant carotenoid
biosynthetic pathway (CBP) in each of our transcriptomes
(Fig. 3). The CBP splits into two branches: the α-carotene
branch (Fig. 3) and the β-carotene branch (Fig. 3).
Biochemical studies of floral carotenoids are lacking in
Gesneriaceae, therefore we cannot confidently assess
which carotenoids are present in Achimenes corollas
without doing biochemical experiments. Our expression
estimates of the CBP enzymes indicate activity of all the
core enzymes in each species (Fig. 3). Some species of
Achimenes, including A. cettoana and A. patens, contain
no carotenoids in the corolla and lower expression of the
CBP enzymes in these species may reflect carotenoid
accumulation in sepals and pollen. In other systems,
particularly Erythranthe, all floral carotenoids are on the
Roberts and Roalson BMC Genomics (2017) 18:240 Page 17 of 26
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

β-carotene branch [100]. We find lower levels of 2
enzymes exclusive to the α-carotene branch (LCYE and
CYP97) compared to the other enzymes found on the
β-carotene branch (LCYB,BCH,ZEP,NXS,andNCED;
Fig. 3). These results may indicate that Achimenes and
other gesneriad species are primarily producing floral
carotenoids via the β-carotene branch, but further
biochemical characterization and experimental studies will
need to be undertaken to support this conclusion.
In general, expression estimates of CBP enzymes are
lower in A. cettoana and A. patens (Fig. 3) and both of
these butterfly-pollinated species contain little to no
visible carotenoid pigment accumulation in their corolla.
Flavonoids (like anthocyanins) absorb UV light and
carotenoids reflect UV light. Presence of anthocyanins in
the petal lobes and absence in the petal tube may reflect
the common use of a ‘bulls-eye’UV pattern to attract
insect pollinators. In contrast, A. erecta and A. misera
contain visible amounts of carotenoids in the corolla tube.
Bee-pollinated A. misera flowers have a clear nectar guide
on the ventral petal formed by the accumulation of carot-
enoids, an important trait for successful bee pollination
[101, 102]. Bird-pollinated flowers, like A. erecta,often
contain combinations of anthocyanins and carotenoids,
with red anthocyanins preventing visitation by bees [103].
Taken together, the pigments contributing to flower color
in Achimenes are important for determining what pollina-
tors visit. Despite butterfly- and bee-pollinated flowers
likely containing a nectar guide, in A. cettoana and
A. patens it appears to be due to flavonoids, while in
A. misera it appears from both flavonoids and carotenoids.
The regulation of carotenoid pigmentation in flowers is
less well understood than the regulation of the ABP.
An R2R3-Myb transcription factor, Reduced Carotenoid
Pigmentation 1 (RCP1), has been the only transcription
factor identified to be involved in flower-specific caroten-
oid biosynthesis [95]. Our analyses identified 9 transcripts
with similarity to RCP1 (Additional file 11). However,
when we look at patterns of coexpression we only find
one candidate (in A. erecta) being coexpressed with any of
the enzymes of the CBP (Additional file 19). Future
genetic experiments will be important to elucidating the
transcriptional regulation of this network in Achimenes
flowers. So far, we have identified potential candidate
transcription factors, but their specific function will need
to be further explored.
Adaptive evolution
The evolution of floral form among the four Achimenes
species is likely influenced by differences in pollinator
availability and preferences. Within the group, there are
distinct floral forms that correspond closely with different
pollination syndromes [10]. Highly dimensional quantitative
data of floral morphology and qualitative data of color and
petal spur size can be reduced into groups that correspond
closely to different pollinators. Flowers of Achimenes are
visited by a number of insects (bees, Apidae; euglossine
bees, Euglossini; butterflies, Lepidoptera) and humming-
birds (Trochilidae) [13]. Observations of pollinator visit-
ation to four Achimenes species provide evidence for the
use of pollination syndromes to separate floral form into
unique groups [13]. Linking protein evolution to the con-
vergent evolution of these different pollination syndromes
may provide evidence for shared or different genetic routes
to these forms. Previous studies have suggested the
pathways involved in pigment production, particularly an-
thocyanins, are involved in pollination syndrome transitions
[45, 74, 75].
Our selection analyses found numerous genes showing
significant signs of molecular evolution (Table 4, Additional
files 18, Additional file 21). However, our analyses did not
provide statistical over- or underrepresentation of any GO
terms within the set of proteins with sites under positive
selection. We do find a number of proteins involved in
various processes during flower development that might
be involved with floral diversification (Table 4). Many
genes have GO terms associated with them involving the
regulation of flower development, anatomical structure
development, and transcription factor activity, among
others (Additional files 18, Additional file 21).
None of the core enzymes of the ABP or the CBP that
we identified were under positive selection. However, a
protein annotated as DFR was identified from A. erecta
(Table 4). The sequence of this protein shares similar
motifs with the DFR enzyme we identified above, but is
not the same transcript (Additional file 6). Given its anno-
tation and similarities it is likely involved in anthocyanin
production, but possibly in a different step of the ABP
than the core part of the pathway we considered here.
Another protein was identified in A. erecta and annotated
as ABCB19 (ABC transporter B family member 19;
Table 4), an auxin efflux transporter with roles in mediat-
ing anthocyanin accumulation in floral tissue [104].
Additionally, in A. cettoana, a protein annotated as
5MAT1 (malonyl-coenzyme:anthocyanin 5-O-glucoside-
6”’-O-malonyltransferase; Table 4) was also identified with
a role in catalyzing the transfer of a malonyl group to the
pelargonidin pigment classes [105]. Like DFR,both
ABCB19 and 5MAT1 are likely involved in anthocyanin
biosynthesis, albeit outside of the core pathway. Other
studies have found signatures of positive selection in the
core ABP enzymes [106], but in the current study we do
not detect any significant evidence.
Some interesting genes involved in flower development
were additionally identified to be under positive selection.
In A. patens, HDA19 (histone deacetylase 19) is a protein
involved in epigenetic repression and plays an important
role in transcriptional regulation, particularly the repression
Roberts and Roalson BMC Genomics (2017) 18:240 Page 18 of 26
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

of several A- and E-class MADS-box genes that control
sepal and petal identity [107]. The role of this histone
deacetylase in the epigenetic modification of floral develop-
mental programs in A. patens is not immediately apparent;
therefore, additional studieswillbeusefultounderstand
the potential myriad roles this gene may play in develop-
ment. Another protein under selection identified from
A. misera,ahomologofFRI (frigida), is involved in flower-
ing time transition [108]. Allelic variation in FRI was
demonstrated in Arabidopsis to be important for natural
variation in flowering time across different latitudes [108].
Flowers are produced on A. misera nearly constantly during
the growing season and the potential role of FRI in develop-
ment will need to be assessed in further experiments.
With expanded sampling of additional Achimenes species,
our analyses of positive selection will be more robust than
thosepresentedhere.Wewereabletoincludesequences
from five species (4 ingroup and 1 outgroup) and compare
gene families that contained members from each of those
species. Our use of site-models allows our detection of
specific amino acids within the protein that may be under-
going positive selection [48, 49]. Evolutionary change can
also happen in the regulatory region of genes, which may
affect the level, timing, and location of gene expression.
Without a genome reference to look for upstream and
downstream mutations that may affect particular genes, we
are unable to currently look at these regions for their effect
on genes involved in floral diversification.
Conclusions
The newly sequenced, assembled, and annotated floral tran-
scriptomes for Achimenes. cettoana, A. erecta, A. misera,
and A. patens provide valuable genomic resources to study
the molecular mechanisms of development, adaptation, and
speciation between closely related species. Comparative
analyses of closely related taxa are important for under-
standing the molecular mechanisms involved in the
evolution and diversification of lineages. The diversity of
floral forms in Achimenes is hypothesized to correspond
to pollinator-driven preferences toward different shapes,
colors, and orientations to provide successful pollination
and fertilization [7, 10]. Large similarities between the floral
transcriptomes in closely related species with diverse floral
phenotypes suggests that these visible differences are, in
part, due to changes in a small set of genes. Combining
analyses of sequence orthology, gene expression, and mo-
lecular evolution have provided initial candidates for future
analyses into the diversification of floral form. Exploration
of the expression patterns for genes relating to flower color
and flower shape has provided interesting patterns corre-
sponding to the floral form of each species. Patterns of ex-
pression for genes involved in anthocyanin and carotenoid
biosynthesis indicate that flower color transitions may be
due to changes in a small set of genes, some of which are
coexpressed together. The datasets presented here also
contribute to the growing number of available genomic
resources for species in the family Gesneriaceae [50, 109–112]
that are study organisms for desiccation tolerance, flower
development, and leaf development. Together, these newly
developed genomic tools provide a valuable resource for
ecological and evolutionary genomics projects, serving as
a starting point to begin understanding phenotypic vari-
ation and the evolutionary genetic forces driving variation
across species and populations in the Gesneriaceae and
other tropical plant lineages.
Methods
Plant material
Flower shape in Achimenes can take many forms, including
funnelform, salverform, tubular, and a number of inter-
mediate forms (Fig. 1). Primary flower color is also quite
variable and is represented by flowers of white, purple, pink,
red, blue, and yellow colors (Fig. 1). We chose to sample
species broadly across Achimenes for the present study in
order to develop initial resources for understanding the
genomic basis for flower diversification. Our sampling
includes A. cettoana, a butterfly pollinated species with
purple-blue salverform flowers (Fig. 1), A. erecta,ahum-
mingbird pollinated species with red salverform flowers
(Fig. 1), A. misera, a bee pollinated species with small, white
funnelform flowers with a purple throat (Fig. 1), and
A. patens, a butterfly pollinated species with large, purple-
pink salverform flowers and a noticeable petal spur (Fig. 1).
These four species represent most of the common flower
shapes and colors seen in the genus, and while they do
not represent all of the possible floral forms, they present
us with a starting point to guide future studies. Vouchers
of each sampled species are deposited in the WR
herbarium with the following identification numbers:
A. cettoana, WR0155; A. erecta, WR0156; A. misera,
WR0157; A. patens, WR0158.
Three stages of flower development were sampled so
that temporal changes in gene expression could be
studied. ‘Immature Bud’(B) stage was the smallest flower
buds that could be distinguished from vegetative buds
(Fig. 1). ‘Stage D’(D)werelargerflowerbudsthatwere
beginning to accumulate pigmentation, the cells in the
corolla tube are elongating, and the petal spur (as in
A. patens) is beginning to develop (Fig. 1). ‘Pre-Anthesis’
(A) flower buds were the largest and fully pigmented and
were collected one-day before anthesis (Fig. 1). Given that
the different species have different flowering times, these
stages are determined from qualitative observations.
Plants were grown in greenhouse conditions under natural
daylight, controlled temperature ranging from 27 to 32 °C,
and >80% humidity. For all experiments, plant material
was harvested directly into liquid nitrogen and subse-
quently stored at -80 °C. To obtain enough fresh material
Roberts and Roalson BMC Genomics (2017) 18:240 Page 19 of 26
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

for RNA extraction, between 2 and 5 flower buds were
sampled from an individual plant.
Library preparation and sequencing
Total RNA was isolated from developing flower buds of
Achimenes by grinding 50–100 mg of tissue frozen in
liquid nitrogen. RNA was then extracted using the Qiagen
RNeasy Plant Mini Kit (Qiagen, Valencia, CA) following
the manufacturers instructions. To avoid genomic DNA
contamination, RNA was treated with Rnase-free Dnase I
(Thermo Fisher Scientific, Waltham, MA). The RNA
integrity was assessed by visualization in 1.0% agarose gels
and RNA Integrity Number (RIN) as measured by an
Agilent 2100 BioAnalyzer (Agilent, Santa Clara, CA).
Ribosomal-depleted RNA samples were prepared using
the Ribo-Zero rRNA Removal Kit for plant leaf material
(Illumina, San Diego, CA). Sequencing libraries were
constructed using the TruSeq RNA-seq sample prep kit
from Illumina (Illumina, San Diego, CA) according to
manufacturers instructions. All stages of library prepar-
ation were performed at the Genome Sequencing and
Analysis Facility (GSAF) at the University of Texas
(Austin, TX). RNAseq libraries were quantified using a
BioAnalyzer 2100 High Sensitivity DNA chip and pooled
based on nM concentrations. Individual libraries were
uniquely barcoded, multiplexed, and sequenced for 100 bp
paired-end reads (2 x 100 bp) using one lane on the
Illumina HiSeq2500 at the GSAF.
De novo assembly
Raw 100 bp paired-end Illumina reads were sorted by
barcode and assessed for quality using the tools imple-
mented in FastQC [113]. The 3′-ends of the reads were
quality trimmed using FASTX-Toolkit [114], removing
any reads that contained bases with Phred scores less
than 20. We also discarded any low quality reads less
than 50 bp long or with less than 80% of bases having a
Phred score greater than 20. Contaminating Illumina
adapter sequences and primers were also trimmed.
Three de novo assemblers were used to construct a
robust set of contigs using different algorithms and k-mer
sizes: Trinity (Tr), Velvet (Vt), and Oases (Oa) [115–117].
Data from the three developmental stages in each species
were concatenated prior to de novo reference assembly.
To provide sets of assembled transcripts, we employed
multiple assemblers using a range of k-mer sizes. For
Tr assembly, we used forward-reverse read orientation
(–SS_lib_type FR) with the default k-mer size of 25. For
Vt assembly, we utilized a multiple k-mer approach, with
separate assemblies performed for k-mer sizes 25, 35, 45,
55, 65, and 75, and specifying a library insert size of 150
(-ins_length 150). Each Vt k-mer assembly was further
assembled using Oa under the default settings.
In order to reduce the redundancy of assemblies and
create sets of primary and secondary transcripts, all assem-
blies were subjected to the EvidentialGene tr2aacds pipeline
[118]. Merged assemblies were produced using the seven
de novo assemblies generated previously. Each de novo
assembly for each species was generated using the three
tissue samples from the same species. The EvidentialGene
pipeline selects a ‘best’set of de novo assembled transcripts,
based on coding potential, from a pool of such sequences.
The algorithm first infers the coding DNA sequences
(CDS) and amino acid sequences for each sequence, and
then removes redundant sequences using the amino acid
information by choosing the best coding sequences from
amongst identical sequences with fastanrdb (exonerate-
2.2.0) [119] and CD-HIT-EST [120]. Self-on-self BLASTn
is then implemented to identify highly similar sequences.
The alignment data and CDS/protein identities are then
used to select and output transcripts classified as ‘main’
(primary; the best transcripts with unique CDS) or
‘alternate’(possible isoforms), and another set classified as
‘dropped’which did not pass the internal filters of the
pipeline. The chosen primary and alternate contigs were
used for further analyses and annotation.
Functional annotation
To annotate transcripts, we conducted a BLAST search of
all unique ‘primary’transcripts against the SwissProt
database (BLASTx, E-value = 1e-06) [30], NCBI non-
redundant (Nr) protein database (BLASTx, E-value =
1e-06) [31], and Plant Non-coding RNA Database
(BLASTn, E-value = 1e-06) [32]. Additionally, the ‘alter-
nate’transcripts sets were searched against the SwissProt
database for annotation. For each sequence we retained
the top five BLAST hits for subsequent analysis. We
placed first priority to the SwissProt database hits for an-
notation, followed by the Nr and PNRD databases because
the SwissProt database contains more GO identities asso-
ciated with the protein hits than either the Nr or PNRD
databases. Sequences with a match in either the SwissProt
or Nr database were subsequently annotated with GO
terms [33] as implemented in Blast2GO v.3.0 [121]. Inter-
ProScan was used to scan transcripts for domain and
motif information that may provide additional GO iden-
tities not attributed using blastx hits alone [33]. GO terms
were assigned based on BLAST hits and InterProScan
results to cover three types of terms: BP, CC, and MF. We
additionally integrated the Second Layer Concept of
Myhre et al. [35] (ANNEX augmentation) to identify,
given the molecular function, biological processes where
the molecular functions are involved, and cellular compo-
nents where they are active. Finally, GO terms were sim-
plified to a smaller set of high-level GO terms (GO slims)
[122]. We obtained GO slims through Blast2GO with the
plant slims developed by the Arabidopsis Information
Roberts and Roalson BMC Genomics (2017) 18:240 Page 20 of 26
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Resource [122]. Additionally, non-coding ribosomal RNAs
and transfer RNAs were detected using RNAmmer [123]
and tRNAscan [124], respectively. We tested for significant
differences in sequence representation for GO categories
between all species with a Chi-square test followed by
using False Discovery Rate (FDR, α= 0.05) adjusted
p-values [125].
Specific enzymes related to anthocyanin pigment pro-
duction were identified through hidden markov models
(HMM) built and trained in HMMER [36]. We searched
our assemblies for proteins identified as homologs to
ANS,DFR,F3′H,andF3′5′H. Protein sequences from
other studies were downloaded from GenBank (Additional
file 4), aligned using MUSCLE [126], and used to create
HMM profiles. These HMM profiles were then used to
search our reference transcriptome to identify possible
candidates proteins. These candidate proteins were then
aligned with candidates from other studies (Additional file
4) using MUSCLE and visually inspected to identify and
correct misaligned regions. Finally, these alignments were
used to construct neighbor-joining trees in Geneious
version R9 [127] with branch support assessed by
performing 100 bootstrap replicates.
Putative proteins involved in flower development,
carotenoid biosynthesis, and petal spur development (taken
from [37]) were identified by BLASTp searches against
Arabidopsis homologs downloaded from the UniProt
database (Additional file 4; www.uniprot.org). The criteria
used to determine the best-hit transcript were (in order):
bit score, E-value, and percent identity.
Members of the R2R3-Myb transcription factor family
that may be involved in floral pigmentation were identified
using HMM models built and trained in HMMER [36].
Proteins with experimental evidence supporting their role
in the transcriptional regulation of floral pigmentation
were downloaded from GenBank (Additional file 4; https://
www.ncbi.nlm.nih.gov/genbank/). The proteins were first
aligned using MUSCLE [126], and then the conserved Myb
domains were extracted, re-aligned using ClustalW [128],
and used to construct a neighbor-joining tree in Geneious
version R9 [127] with branch support assessed by perform-
ing 100 bootstrap replicates.
Orthogroup identification
We next identified conserved orthogroups from the sets
of translated proteins identified in each Achimenes species
using OrthoFinder v.0.3.0 [129]. This method solves the
problem of gene length bias in BLAST searches by nor-
malizing the bit scores by both gene length and phylogen-
etic distance and outperforms the more commonly used
OrthoMCL in accuracy and speed [129]. Orthologs and
paralogs were determined for each species individually as
well as in five-way comparisons. In the comparative ana-
lyses, we used a corolla transcriptome from Erythranthe
lewisii LF10 (15 mm corolla; available from http://
www.monkeyflower.uconn.edu/resources) as comparison
[86]. We chose E. lewisii for comparison because it is a
flower-specific transcriptome that is phylogenetically close
to Achimenes (both are members of the Order Lamiales).
Protein coding sequences were produced for E. lewisii
using TransDecoder v.2.0 [130], under default settings.
Quantifying and comparing gene expression patterns
Trimmed, high-quality reads from individual stage-specific
samples (B, D, and A) were independently mapped onto
each primary reference transcriptome using the ungapped
alignment software bowtie [42]. We used the abundance of
reads derived from each locus to estimate gene expression
and calculate transcripts per kilobase million (TPM) values
with the program RSEM (RNA-Seq by Expectation
Maximization) [43]. The numbers of reads mapped per
library were normalized by the trimmed mean of M-values
normalization method (TMM) [131]. Genes were consid-
ered expressed in a developmental stage if they had a
normalized TPM ≥0.01 in that stage. Expression estimates
for floral developmental genes in individual species were
transformed to Z-scores for heatmap representations.
Transcripts with estimated expression values ≤0.01 were
removed prior to clustering. To cluster sets of co-expressed
genes within each species, we performed clustering using
HTSCluster [44]. Unlike other commonly used clustering
algorithms (e.g., k-means, hierarchical), HTSCluster is a
model based clustering approach that uses Poisson mixture
models to cluster sequences using expression estimates and
selects the appropriate number of clusters using slope
heuristics (Djump and DDSE) [46]. We ran HTSCluster
using the EM [45] algorithm for parameter estimation and
tested cluster numbers ranging from K=1,2,…, 60. From
5 independent runs, we selected the model and associated
cluster number that had the highest log-likelihood. We
used both the Djump and DDSE criteria to select the
number of clusters for each run. The degree of certitude in
cluster assignment was additionally evaluated using the
maximum conditional probabilities of cluster membership
for the genes assigned to each cluster.
Detecting genes under selection
Each orthogroup identified with the OrthoFinder five-way
analysis was run through a pipeline to identify protein
sites potentially undergoing selection. The pipeline first
takes the CDS sequences and inferred homology relation-
ships and filtered these based on numeric, phylogenetic,
and quality criteria to remove spurious data. We chose to
keep proteins having a complete coding region (strings in
multiples of 3), a minimum of 5 species and 5 sequences,
and mean sequence divergence of ≤60%. Each satisfactory
orthogroup then undergoes multiple sequence alignment
using MUSCLE [126], protein-guided codon alignment
Roberts and Roalson BMC Genomics (2017) 18:240 Page 21 of 26
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

using TrimAl [132], and phylogenetic tree reconstruction
using dnaml from Phylip [133]. Finally these orthogroups
are analyzed for signatures of selection using the site-
models implemented in PAML v.4.6 [134]. For our
analyses, we used the M1a (neutral), M2a (selection), M7
(beta), and M8 (beta + ω) models implemented in codeml
[48, 49]. Model M1a was compared to M2a and M7 was
compared to M8. Significance differences in model fit for
each comparison were assessed using a likelihood ratio
test followed by FDR correction for multiple hypothesis
testing (α=0.05).
Gene ontology enrichment analyses
We used the FatiGO [135] package as integrated with
Blast2GO to assess enrichment of GO terms in the proteins
identified during 1) orthogroup clustering, 2) coexpression
clustering, or 3) detection of sites under positive selection.
Previously for each Achimenes transcriptome, we obtained
a list of annotated transcripts with associated GO identities.
This information was then divided into three GO maps
based on the three GO domains: 1) BP, 2) CC, and 3) MF.
Each analysis was performed using a two-tailed Fisher’s
ExactTestusingFDR-correctedp-values (α≤0.05). Both
over- and underrepresented GO terms were identified for
each cluster or group relative to the whole transcriptome
background.
Additional files
Additional file 1: Table S1. Detailed sequencing and assembly
statistics. A. Trinity assembly. B. Velvet and Oases assemblies. C.
EvidentialGene merged assembly. (XLSX 41 kb)
Additional file 2: Table S2. Number of contigs detected as rRNAs and
tRNAs in each transcriptome. (XLSX 33 kb)
Additional file 3: Figure S1. Counts and proportion of level 2 Gene
Ontology annotations for Achimenes transcriptomes. (PDF 361 kb)
Additional file 4: Table S3. Protein homologs downloaded from
GenBank used for HMM profile searches. A. ANS. B. DFR.C. F3′Hand F3′5′
H.D. R2R3-Mybs. (XLSX 48 kb)
Additional file 5: Figure S2. Neighbor-joining tree of anthocyanidin
synthase (ANS) gene family. Putative Achimenes ANS orthologs are
highlighted in red. Bootstrap support >50 are indicated above
branches. (PDF 455 kb)
Additional file 6: Figure S3. Neighbor-joining tree of dihydroflavonol
4-reductase (DFR) gene family. Putative Achimenes DFR orthologs are
highlighted in red. Boostrap support >50 are indicated above branches.
(PDF 330 kb)
Additional file 7: Figure S4. Neighbor-joining tree of flavonoid 3′-
hydroxylase (F3′H) and flavonoid 3′,5′-hydroxylase (F3′5′H) gene family. Putative
Achimenes F3′Hand F3′5′Horthologs are hi ghlighted in red and bl ue,
respectively. Bootstrap support >50 are indicated above branches.
(PDF 1175 kb)
Additional file 8: Figure S5. xAligned protein sequences for Achimenes
F3′H, F3′5′H, and DFR. Amino acid substitutions between the sequences of
A. cettoana, A. erecta, A. misera, and A. patens are highlighted in blue, red,
black, and pink, respectively. A, F3′H;B,F3′5′H;C,DFR. (PDF 3121 kb)
Additional file 9: Table S4. Homologs of flower development genes
identified in Achimenes. Included are the expression domain, primary role,
and gene family. (XLSX 60 kb)
Additional file 10: Figure S6. Expression of genes involved in flower
development in Achimenes. (PDF 346 kb)
Additional file 11: Figure S7. Neighbor-joining tree of R2R3-Mybs in
Achimenes. Putative orthologs involved in anthocyanin and carotenoid
biosynthesis are highlighted in blue and orange, respectively. Bootstrap
support >50 are indicated above branches. (PDF 921 kb)
Additional file 12: Table S5. Significantly enriched Gene Ontology
terms for sequences in the “Core”transcriptome after orthogroup
classification. Terms are enriched if they have FDR-corrected p-values < 0.05
(Fisher’s Exact Test). Those terms that are overrepresented when all species
are analyzed tog ether (“Combined”column) are in bold. (XLSX 16 kb)
Additional file 13: Table S6. Significantly enriched Gene Ontology
terms for sequences in the “Shared Achimenes”clusters after orthogroup
classification. Terms are enriched if they have FDR-corrected p-values <
0.05 (Fisher’s Exact Test). Those terms that are overrepresented when all
species are analyzed together (“Combined”column) are in bold. (XLSX 48 kb)
Additional file 14: Table S7. Significantly enriched Gene Ontology
terms for sequences that were unassigned during orthogroup classification.
Terms are enriched if they have FDR-corrected p-values < 0.05 (Fisher’sExact
Test). (XLSX 38 kb)
Additional file 15: Figure S8. Coexpression clusters for Achimenes
determined using Poisson mixture models. Gene profiles are depicted as
boxplots. Conditions are as follows: 1, Bud stage; 2, Stage D; and 3,
Pre-Anthesis stage. A, A. cettoana;B,A. erecta;C,A. misera;D,A. patens.
(PDF 4192 kb)
Additional file 16: Figure S9. Maximum conditional probability of
cluster membership assigned by coexpression clustering using Poisson
mixture models. A, Achimenes cettoana;B,A. erecta;C,A. misera;D,A.
patens. (PDF 1176 kb)
Additional file 17: Table S8. Detailed model selection statistics and
Gene Ontology enrichment for coexpression clustering. A. A. cettoana.B.
A. erecta.C. A. misera.D. A. patens. (XLSX 54 kb)
Additional file 18: Table S9. Gene Ontology enrichment for coexpression
clustering. A. A. cettoana. B. A. erecta. C. A. misera. D. A. patens. (XLSX 228 kb)
Additional file 19: Table S10. Coexpression clusters for candidates
involved in anthocyanin biosynthesis, carotenoid biosynthesis, and spur
development. (XLSX 40 kb)
Additional file 20: Table S11. Annotated proteins with sites under
selection using PAML for M1a vs. M2a comparison. A. Achimenes cettoana.B.
Achimenes erecta. C. Achimenes misera. D. Achimenes patens. E. Erythranthe
lewisii. (XLSX 60 kb)
Additional file 21: Table S12. Annotated proteins with sites under
selection using PAML for M7 vs. M8 comparison. A. Achimenes cettoana.
B. Achimenes erecta. C. Achimenes misera. D. Achimenes patens. E.
Erythranthe lewisii. (XLSX 75 kb)
Abbreviations
ABP: Anthocyanin biosynthetic pathway; ANS: Anthocyanidin synthase;
BP: Biological process; CBP: Carotenoid biosynthetic pathway; CC: Cellular
component; CDS: Coding DNA sequences; DDSE: Data driven slope estimation;
DFR: Dihydroflavonol 4-reductase; Djump: Dimension jump; EM: Expectation
maximization algorithm; F3′5′H:Flavonoid3′,5′-hydroxylase; F3′H:Flavonoid
3′-hydroxylase; FDR: False discovery rate; GO: Gene ontology; HMM: Hidden
markov models; MF: Molecular function; NB: Negative binomial; Nr: NCBI
non-redundant protein database; Oa: Oases assembly; PNRD: Plant Non-coding
RNA Database; TMM: Trimmed mean of M-values normalization method;
TPM: Transcripts per kilobase million; Tr: Trinity assembly; Ve: Velvet assembly
Acknowledgements
Joanna L. Kelley, Andrew McCubbin, and Amit Dhingra provided helpful
discussion for this project. Brian W. Davis provided helpful comments on
figure construction. The staff at the Genomic Sequencing and Analysis
Facility (GSAF) at the University of Texas at Austin provided library preparation
and sequencing services. We are grateful to Mohammed Bakkali and one
anonymous reviewer for their critical review and constructive comments.
Roberts and Roalson BMC Genomics (2017) 18:240 Page 22 of 26
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Funding
A Global Plant Sciences Initiative Fellowship awarded to WRR and funding
from the School of Biological Sciences (WSU) to EHR supported this work.
These funding sources had no role in the design, data collection, analyses,
interpretation of data, and writing of the manuscript.
Availability of data and materials
The datasets generated during and analyzed during the current study are
available in the NCBI Short Read Archive repository, available from accession
SRP083265. The contig assemblies generated during the current study are
available from the corresponding author upon request. The datasets
supporting the conclusions of this article are included within the article
and its additional files.
Authors’contributions
WRR collected grew plant material, extracted RNA, performed all analyses, and
wrote the manuscript. WRR and EHR conceived the project, designed experiments,
and interpreted the findings. All authors read and approved the final manuscript.
Competing interests
The authors declare that they have no competing interests.
Consent for publication
Not applicable.
Ethics approval and consent to participate
Non-commercial plant material used in this study was acquired from
established horticultural collections of Gesneriaceae plants cultivated in the
School of Biological Sciences at Washington State University. Vouchers of
each species have been placed in the WS herbarium.
Publisher’sNote
Springer Nature remains neutral with regard to jurisdictional claims in published
maps and institutional affiliations.
Received: 17 September 2016 Accepted: 11 March 2017
References
1. Barrett CH, Harder LD, Worley AC. The comparative biology of pollination
and mating in flowering plants. Philos T Roy Soc B. 1996;351:1271–80.
2. Regel PJ. Ecology and evolution of flowering plant dominance. Science.
1977;196:622–9.
3. Endress PK, Matthews ML. First steps toward a floral structural characterization
of the major Rosid subclades. Plant Syst Evol. 2006;260:223–51.
4. Becker A, Thiessen G. The major clades of MADS-box genes and their role in
the development and evolution of flowering plants. Mol Biol Evol. 2003;
29:464–89.
5. Harrison J, Möller M, Cronk QCB. Evolution and development of floral diversity
in Streptocarpus and Saintpaulia. Ann Bot-London. 1999;84:49–60.
6. Clark JL, Roalson EH, Pritchard RA, Coleman CL, Teoh V-H, Matos J. Independent
origin of radial floral symmetry in the Gloxinieae (Gesnerioideae: Gesneriaceae)
is supported by the rediscovery of Phinaea pulchella in Cuba. Syst Bot.
2011;36:757–67.
7. Roalson EH, Roberts WR. Distinct processes drive diversification in different
clades of Gesneriaceae. Syst Biol. 2016;65:662–84.
8. Wiehler H. A report on the classification of Achimenes, Eucodonia, Gloxinia,
Goyazia, and Anetanthus (Gesneriaceae). Selbyana. 1976;1:374–404.
9. Wiehler H. A synopsis of the neotropical Gesneriaceae. Selbyana. 1983;6:1–219.
10. Roalson EH, Skog LE, Zimmer EA. Phylogenetic relationships and the
diversification of floral form in Achimenes (Gesneriaceae). Syst Bot.
2003;28:593–608.
11. Perret M, Chautems A, Spichiger R, Barraclough TG, Savolainen V. The
geographical pattern of speciation and floral diversification in the Neotropics:
the Tribe Sinningieae (Gesneriaceae) as a case study. Evolution. 2007;61:1641–60.
12. Ramírez Roa MA. Revision de Achimenes (Gesneriaceae). Thesis: Universidad
Nacional Autonoma de Mexico D.F; 1987.
13. Martén-Rodríguez S, Quesada M, Castro AA, Lopezaraiza-Mikel M, Fenster
CB. A comparison of reproductive strategies between island and mainland
Caribbean Gesneriaceae. J Ecol. 2015;103:1190–204.
14. Marra NJ, Romero A, DeWoody JA. Natural selection and the genetic basis
of osmoregulation in heteromyid rodents as revealed by RNA-seq. Mol Ecol.
2014;23:2699–711.
15. Davies KTJ, Bennett NC, Tsagkogeorga G, Rossiter SJ, Faulkes CG. Family-wide
molecular adaptations to underground life in African mole-rats revealed by
phylogenomic analysis. Mol Biol Evol. 2015;32:3089–107.
16. Davidson RM, Gowda M, Moghe G, Lin H, Vaillancourt B, Shiu SH, Jiang N,
Robin BC. Comparative transcriptomics of three Poaceae species reveals
patterns of gene expression evolution. Plant J. 2012;71:492–502.
17. Li QG, Zhang L, Li C, Dunwell JM, Zhang YM. Comparative genomics suggests
that an ancestral polyploidy event leads to enhanced root nodule symbiosis in
the Papilionoideae. Mol Biol Evol. 2013;30:2602–11.
18. Yang Z, Wafula EK, Honaas LA, Zhang H, Das M, Fernandez-Aparicio M, Huang
K, Bandaranayake PCG, Wu B, Der JP, Clarke CR, Ralph PE, Landherr L, Altman
NS,TimkoMP,YoderJI,WestwoodJH,dePamphilisCW.Comparative
transcriptome analyses reveal core parasitism genes and suggest gene
duplication and repurposing as sources of structural novelty. Mol Biol
Evol. 2015;32:767–90.
19. Rowland LJ, Alkharouf N, Darwish O, Ogden EL, Polashock JJ, Bassil NV, Main
D. Generation and analysis of blueberry transcriptome sequences from
leaves, developing fruit, and flower buds from cold acclimation through
deacclimation. BMC Plant Biol. 2012;12:46.
20. Zhang XM, Zhao L, Larson-Rabin Z, Li DZ, Guo ZH. De novo sequencing and
characterization of the floral transcriptome of Dendrocalamus latiflorus
(Poaceae: Bambusoideae). PLoS One. 2012;7, e42082.
21. Díaz-Riquelme J, Martínez-Zapater JM, Carmona MJ. Transcriptional analysis
of tendril and inflorescence development in grapevine (Vitis vinifera L.). PLoS
One. 2014;9, e92339.
22. Zahn LM, Ma X, Altman NS, Zhang Q, Wall PK, Tian D, Gibas CJ, Gharaibeh R,
Leebens-Mack JH, de Pamphilis CW, Ma H. Comparative transcriptomics
among floral organs of the basal eudicot Eschscholzia californica as
reference for floral evolutionary developmental studies. Genome Biol.
2010;11:R101.
23. Qiu WM, Zhu AD, Wang Y, Chai LJ, Ge XX, Deng XX, Guo WW. Comparative
transcript profiling of gene expression between seedless Ponkan mandarin
and its seedy wild type during floral organ development by suppression
subtractive hybridization and cDNA microarray. BMC Genomics.
2012;13:397.
24. Logacheva MD, Kasianov AS, Vinogradov DV, Samigullin TH, Gelfand MS,
Makeev VJ, Penin AA. De novo sequencing and characterization of floral
transcriptome in two species of buckwheat (Fagopyrum). BMC Genomics.
2011;12:30.
25. Vining KJ, Romanel E, Jones RC, Klocko A, Alves-Ferreira M, Hefer CA,
Aamarasinghe V, Dharmawardhana P, Naithani S, Ranik M, Wesley-Smith
J, Solomon J, Jaiswai P, Myburg AA, Straus SH. The flor al transcriptome
of Eucalyptus grandis. New Phytol. 2014;206:1406–22.
26. Wei W, Qi X, Wang L, Zhang Y, Hua W, Li D, Lv H, Zhang X. Characterization
of the sesame (Sesamum indicum) global transcriptome using Illumina paired-
end sequencing and development of EST-SSR markers. BMC Genomics.
2011;12:451.
27. Bhide A, Schliesky S, Reich M, Weber APM, Becker A. Analysis of the floral
transcriptome of Tarenaya hassleriana (Cleomaceae), a member of the sister
group to the Brassicaceae: towards understanding the base of
morphological diversity in Brassicales. BMC Genomics. 2014;15:140.
28. Ness RW, Siol M, Barrett SCH. De novo sequence assembly and characterization
of the floral transcriptome in cross- and self-fertilizing plants. BMC Genomics.
2011;12:298.
29. Kobayashi MJ, Takeuchi Y, Kenta T, Kume T, Diway B, Shimizu KK. Mass flowering
of the tropical tree Shorea beccariana was preceded by expression changes in
flowering and drought-responsive genes. Mol Ecol. 2013;22:4767–82.
30. The UniProt Consortium. UniProt: a hub for protein information. Nucleic
Acids Res. 2015;43:D204–12.
31. Pruitt KD, Tatusova T, Maglott DR. NCBI Reference Sequence (RefSeq): a
curated non-redundant sequence database of genomes, transcripts and
proteins. Nucleic Acids Res. 2005;33:D501–4.
32. Yi X, Zhang Z, Ling Y, Xu W, Su Z. PNRD: a plant non-coding RNA database.
Nucleic Acids Res. 2015;43:D982–9.
33. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP,
Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A,
Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G. Gene
Ontology: tool for the unification of biology. Nat Genet. 2000;25:25–9.
Roberts and Roalson BMC Genomics (2017) 18:240 Page 23 of 26
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

34. Quevillon E, Silventoinen V, Pillai S, Harte N, Mulder N, Apweller R, Lopez R.
InterProScan: protein domains identifier. Nucleic Acids Res. 2005;33
Suppl 2:W116–20.
35. Myhre S, Tveit H, Mollestad T, Lægreid A. Additional gene ontology structure
for improved biological reasoning. Bioinformatics. 2006;22:2020–7.
36. Eddy SR. Profile Hidden Markov Models. Bioinformatics. 1998;14:755–63.
37. Yant L, Collani S, Puzey J, Levy C, Kramer EM. Molecular basis for three-
dimensional elaboration of the Aquilegia petal spur. P Roy Soc B-Biol Sci.
2015;282:20142778.
38. Hay A, Tsiantis M. KNOX genes: versatile regulators of plant development
and diversity. Development. 2010;137:3153–65.
39. Eklund DM, Ståldal V, Valsecchi I, Cierlik I, Eriksson C, Hiratsu K, Ohme-Takagi
M, Sundström JF, Thelander M, Ezcurra I, Sundberg E. The Arabidopsis
thaliana STYLISH1 protein acts as a transcriptional activator regulating auxin
biosynthesis. Plant Cell. 2010;22:349–63.
40. Choe S. Signal-transduction pathways toward the regulation of brassinosteroid
biosynthesis. J Plant Biol. 2007;50:225–9.
41. YinY,VafeadosD,TaoY,YoshidaS,AsamiT,ChoryJ.Anewclassof
transcription factors mediates brassinosteroid-regulated gene expression
in Arabidopsis. Cell. 2005;120:249–59.
42. Langmead B, Trapnell C, Pop M, Salzberg SL. Ultrafast and memory-efficient
alignment of short DNA sequences to the human genome. Genome Biol.
2009;10:R25.
43. Li B, Dewey CN. RSEM: accurate transcript quantification from RNA-Seq data
with or without a reference genome. BMC Bioinformatics. 2011;12:323.
44. Rau A, Maugis-Rabusseau C, Martin-Magniette ML, Celeux G. Co-expression
analysis of high-throughput transcriptome sequencing data with Poisson
mixture models. Bioinformatics. 2015;31:1420–7.
45. Dempster AP, Laird NM, Rubin DB. Maximum likelihood from incomplete
data via the EM algorithm. J Roy Stat Soc B Met. 1977;39:1–38.
46. Baudry JP, Maugis C, Bertrand M. Slope heuristics: overview and implementation.
Stat Comput. 2012;22:455–70.
47. Anisimova M, Bielawski JP, Yang Z. Accuracy and power of the likelihood
ratio test in detecting adaptive molecular evolution. Mol Biol Evol.
2001;18:1585–92.
48. Yang Z, Nielsen R, Goldman N, Pedersen AMK. Codon-substitution models
for heterogeneous selection pressure at amino acid sites. Genetics.
2000;155:431–49.
49. Yang Z, Wong WSW, Nielsen R. Bayes empirical Bayes inference of amino
acid sites under positive selection. Mol Biol Evol. 2005;22:1107–18.
50. Alexandre H, Vrignaud J, Mangin B, Joly S. Genetic architecture of
pollination syndrome transition between hummingbird- speciali st and
generalist species in the genus Rhytidophyllum (Gesneriaceae). Peer J.
2015;3, e1028.
51. Tanase K, Nishitani C, Hirakawa H, Isobe S, Tabata S, Ohmiya A, Onozaki T.
Transcriptome analysis of carnation (Dianthus caryophyllus L.) based on
next-generation sequencing technology. BMC Genomics. 2012;13:292.
52. von Bakel H, Stout JM, Cote AG, Tallon CM, Sharpe AG, Hughes TR, Page JE. The
draft genome and transcriptome of Cannabis sativa. Genome Biol. 2011;12:R102.
53. Parchman TL, Geise KS, Grahnen JE, Benkman CW, Buerkle CA. Transcriptome
sequencing in an ecologically important tree species: assembly, annotation,
and marker discovery. BMC Genomics. 2010;11:180.
54. Zhang J, Wu K, Zeng S, da Silva JA T, Zhao X, Tian CE, Xia H, Duan J.
Transcriptome analysis of Cymbidium sinense and its application to the
identification of genes associated with floral development. BMC Genomics.
2013;14:279.
55. Melicher D, Torson AS, Dworkin I, Bowsher JH. A pipeline for the de novo
assembly of the Themira biloba (Sepsidae: Diptera) transcriptome using a
multiple k-mer length approach. BMC Genomics. 2014;15:188.
56. Nakasugi K, Crowhurst R, Bally J, Waterhouse P. Combining transcriptome
assemblies from multiple de novo assemblers in the allo-tetraploid plant
Nicotiana benthamiana. PLoS One. 2014;9, e91776.
57. Chen S, McElroy JS, Dane F, Peatman E. Optimizing transcriptome assemblies
for Eleusine indica leaf and seedling by combining multiple assemblies from
three de novo assemblers. Plant Genome. 2015;8.
58. Zhao QY, Wang Y, Kong YM, Luo D, Li X, Hao P. Optimizin g de novo
transcriptome assembly from short-read RNA-Seq data: a comparative
study. BMC Genomics. 2011;12 Suppl 14:S2.
59. Eisen MB, Spellman PT, Brown PO, Botstein D. Cluster analysis and display of
genome-wide expression patterns. P Natl Acad Sci USA.
1998;95:14863–8.
60. Jiang D, Tang C, Zhang A. Cluster analysis for gene expression data: a
survey. IEEE T Knowl Data En. 2004;16:1370–86.
61. MacQueen JB. Some methods for classification and analysis of multivariate
observations. In: Le Cam LM, Neyman J, editors. Proceedings of the 5
th
Berkeley Symposium on Mathematical Statistics and Probability, number 1.
Berkeley: Univ Calif Press; 1967. p. 281–97.
62. Ward JH. Hierarchical grouping to optimize and objective function. J Am
Stat Assoc. 1963;58:236–44.
63. Guzzi PH, Masciari E, Mazzeo GM, Zaniolo C. A discussion on the biological
relevance of clustering results. In: Bursa M, Khuri S, Renda ME, editors.
International Conference on Information Technology in bio- and medical
informatics. Gewerbestrasse: Springer Intl Pub; 2014. p. 30–44.
64. Si Y, Liu P, Li P, Brutnell TP. Model-based clustering for RNA-seq data.
Bioinformatics. 2014;30:197–205.
65. Marioni JC, Mason CE, Mane SM, Stephens M, Gilad Y. RNA-seq: An assessment of
technical reproducibility and comparison with gene expression arrays. Genome
Res. 2008;18:1509–17.
66. Anders S, Huber W. Differential expression analysis for sequence count data.
Genome Biol. 2010;11:R106.
67. Fukushima A, Nishizawa T, Hayakumo M, Hikosaka S, Saito K, Goto E, Kusano
M. Exploring tomato gene functions based on coexpression modules using
graph clustering and differential coexpression approaches. Plant Physiol.
2012;158:1487–502.
68. Coneva V, Simopoulos C, Casaretto JA, El-Kereamy A, Guevara DR, Cohn J,
Zhu T, Guo L, Alexander DC, Bi YM, McNicholas PD, Rothstein SJ. Metabolic
and co-expression network-based analyses associated with nitrate response
in rice. BMC Genomics. 2014;15:1056.
69. Langfelder P, Horvath S. WGCNA: an R package for weighted correlated
network analysis. BMC Bioinformatics. 2008;9:559.
70. Hollender CA, Kang C, Darwish O, Geretz A, Matthews BF, Slovin J, Alkharouf
N, Liu Z. Floral transcriptomes in woodland strawberry uncover developing
receptacle and anther gene networks. Plant Physiol. 2014;165:1062–75.
71. Fan Z, Li J, Li X, Wu B, Wang J, Liu Z, Yin H. Genome-wide transcriptome
profiling provides insights into floral bud development of summer-
flowering Camellia azalea. Sci Reports. 2015;2015:5.
72. Huang YJ, Liu LL, Huang JQ, Wang ZJ, Chen FF, Zhang QX, Zheng BS, Chen
M. Use of transcriptome sequencing to understand the pistillate flowering
in hickory (Carya cathayensis Sarg.). BMC Genomics. 2013;14:691.
73. Yoo MJ, Chanderbali AS, Altman NS, Soltis PS, Soltis DE. Evolutionary trends
in the floral transcriptome: insights from one of the basalmost angiosperms,
the water lily Nuphar advena (Nymphaeaceae). Plant J. 2010;64:687–98.
74. Annick D, Remay A, Raymond O, Balzergue S, Chauvet A, Maene M, Pécrix Y,
Yang SH, Jeauffre J, Thouroude T, Boltz V, Martin-Magniette ML, Janczarski S,
Legeai F, Renou JP, Vergne P, Le Bris M, Foucher F, Bendahmane M.
Genomic approach to study floral development genes in Rosa sp. PLoS
One. 2011;6, e28455.
75. Kater MM, Dreni L, Colombo L. Functional conservation of MADS-box factors
controlling floral organ identity in rice and Arabidopsis. J Exp Bot.
2006;57:3433–44.
76. Urbanus SL, Dinh QD, Angenent GC, Immink RG. Investigation of MADS
domain transcription factor dynamics in the floral meristem. Plant Signal
Behav. 2010;5:1260–2.
77. Ding B, Mou F, Sun W, Chen S, Peng F, Bradshaw HD, Yuan YW. A dominant-
negative actin mutation alters corolla tube width and pollinator visitation in
Mimulus lewisii. New Phytol. In press.
78. Golz JF, Keck EJ, Hudson A. Spontaneous mutations in KNOX genes give rise
to a novel floral structure in Antirrhinum. Curr Biol. 2002;12:515–22.
79. Box MS, Dodsworth S, Rudall PJ, Bateman RM, Glover B. Characterization of
Linaria KNOX genes suggest a role in petal-spur development. Plant J.
2011;68:703–14.
80. Grotewald E. The genetics and biochemistry of floral pigments. Annu Rev
Plant Biol. 2006;57:761–80.
81. Saito K, Yonekura-Sakakibara K, Nakabayashi R, Higashi Y, Yamazaki M, Tohge T,
Fernie AR. The flavonoid biosynthetic pathway in Arabidopsis: structural and
genetic diversity. Plant Physiol Bioch. 2013;72:21–34.
82. Whittall JB, Voelckel C, Kliebenstein DJ, Hodges SA. Convergence, constraint
and the role of gene expression during adaptive radiation: floral
anthocyanins in Aquilegia. Mol Ecol. 2006;15:4645–57.
83. Streisfeld MA, Rausher MD. Altered trans-regulatory control of gene expression
in multiple anthocyanin genes contributes to adaptive flower color evolution
in Mimulus aurantiacus. Mol Biol Evol. 2009;26:433–44.
Roberts and Roalson BMC Genomics (2017) 18:240 Page 24 of 26
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

84. Smith SD, Rausher MD. Gene loss and parallel evolution contribute to
species differences in flower color. Mol Biol Evol. 2011;28:2799–810.
85. Smith SD, Rausher MD. Functional evolution of an anthocyanin pathway
enzyme during a flower color transition. Mol Biol Evol. 2013;30:602–12.
86. Yuan YW, Sagawa JM, Frost L, Vela JP, Bradshaw HD. Transcriptional control
of floral anthocyanin pigmentation in monkeyflowers (Mimulus). New
Phytol. 2014;204:1013–27.
87. Streisfeld MA, Rausher MD. Genetic changes contributing to the parallel
evolution of red floral pigmentation among Ipomoea species. New Phytol.
2009;183:751–63.
88. Des Marais DL, Rausher MD. Parallel evolution at multiple levels in the origin of
hummingbird pollinated flowers in Ipomoea. Evolution. 2010;64:2044–54.
89. Wessinger CA, Hileman LC, Rausher MD. Identification of major quantitative
trait loci underlying floral pollination syndrome divergence in Penstemon.
Philos T Roy Soc B. 2014;369:20130349.
90. Wessinger CA, Rausher MD. Ecological transition predictably associated with
gene degeneration. Mol Biol Evol. 2015;32:347–54.
91. Lowry DB, Sheng CC, Lasky JR, Willis JH. Five anthocyanin polymorphisms
are associated with an R2R3-MYB cluster in Mimulus guttatus (Phrymaceae).
Am J Bot. 2012;99:82–91.
92. Seitz C, Eder C, Deimi B, Kellner S, Martens S, Forkmann G. Cloning,
functional identification and sequence analysis of flavonoid 3′-hydroxylase
and flavonoid 3′,5′-hydroxylase cDNAs reveals independent evolution of
flavonoid 3′,5′-hydroxylase in the Asteraceae family. Plant Mol Biol.
2006;61:365–81.
93. Seitz C, Ameres S, Schlangen K, Forkmann G, Halbwirth H. Multiple evolution of
flavonoid 3′,5′-hydoxylase. Planta. 2015;242:561–73.
94. Stankowski S, Streisfeld MA. Introgressive hybridization facilitates adaptive
divergence in a recent radiation of monkeyflowers. P Roy Soc B-Biol Sci.
2015;282:20151666.
95. Sagawa JM, Stanley LE, LaFountain AM, Frank HA, Liu C, Yuan YW. An R2R3-
MYB transcription factor regulates carotenoid pigmentation in Mimulus lewisii
flowers. New Phytol. 2015;209:1049–57.
96. Quattrocchio F, Wing J, van der Woude K, Souer E, de Vetten N, Mol J, Koes
R. Molecular analysis of the anthocyanin2 gene of Petunia and its role in the
evolution of flower color. Plant Cell. 1999;11:1433–44.
97. Schwinn K, Venail J, Shang Y, Mackay S, Alm V, Butelli E, Oyama R, Bailey P,
Davies K, Martin C. A small family of MYB-regulatory genes controls floral
pigmentation intensity and patterning in the genus Antirrhinum. Plant Cell.
2006;18:831–51.
98. Dall’Osto L, Fiore A, Cazzinga S, Giuliano G, Bassi R. Different roles of alpha-
and beta-branch xanthophylls in photosystem assembly and
photoreception. J Biol Chem. 2007;282:35056–68.
99. Glover BJ. Understanding flowers and flowering: an integrated approach.
Oxford, UK: Oxford University Press; 2014.
100. LaFountain AM, Frank HA, Yuan Y-W. Carotenoid composition of the flowers
of Mimulus lewisii and related species: implications regarding the prevalence
and origin of two unique, allenic pigments. Arch Biochem Biophys.
2015;573:32–9.
101. Bradshaw HD, Wilbert M, Otto KG. Genetic mapping of floral traits associated
with reproductive isolation in monkeyflowers (Mimulus). Nature. 1995;375:31.
102. Owen CR, Bradshaw HD. Induced mutations affecting pollinator choice in
Mimulus lewisii (Phrymaceae). Arthropod-Plant Inte. 2011;5:235–44.
103. Cronk Q, Ojeda I. Bird-pollinated flowers in an evolutionary and molecular
context. J Exp Bot. 2008;59:715–27.
104. Lin R, Wang H. Two homologous ATP-binding cassette transporter proteins,
AtMDR1 and AtPGP1, regulate Arabidopsis photomorphogenesis and root
development by mediating polar auxin transport. Plant Physiol. 2005;138:949–64.
105. Suzuki H, Nakayama T, Yonekura-Sakakibara K, Fukui Y, Nakamura N, Nakao
M, Tanaka Y, Yamaguchi MA, Kusumi T, Nishino T. Malonyl-CoA:anthocyanin
5-O-glucoside-6”’-O-malonyltransferase from scarlet sage (Salvia splendens)
flowers. Enzyme purification, gene cloning, expression, and characterization.
J Biol Chem. 2001;276:49013–9.
106. Ho WW, Smith SD. Molecular evolution of anthocyanin pigmentation genes
following losses of flower color. BMC Evol Biol. 2016;16:98.
107. Krogan NT, Hogan K, Long JA. APETALA2 negatively regulates multiple floral
organ identity genes in Arabidopsis by recruiting the co-repressor TOPLESS
and the histone deacetylase HDA19. Development. 2012;139:4180–90.
108. Johanson U, West J, Lister C, Michaels S, Amasino R, Dean C. Molecular
analysis of FRIGIDA, a major determinant of natural variation in Arabidopsis
flowering time. Science. 2000;290:344–7.
109. Chiara M, Horner DS, Spada A. De novo assembly of the transcriptome of
the non-model Streptocarpus rexii employing a novel heuristic to recover
locus-specific transcript clusters. PLoS One. 2013;8, e80961.
110. Ai B, Gao Y, Zhang X, Tao J, Kang M, Huang H. Comparative transcriptome
resources of eleven Primulina species, a group of ‘stone plants’from a
biodiversity hot spot. Mol Ecol Resour. 2014;15:619–32.
111. Xiao L, Yang G, Zhang L, Yang X, Zhao S, Ji Z, Zhou Q, Hu M, Wang Y, Chen
M, Xu Y, Jin H, Xiao X, Hu G, Bao F, Hu Y, Wan P, Li L, Deng X, Kuang T,
Xiang C, Zhu JK, Oliver MJ, He Y. The resurrection genome of Boea
hygrometrica: a blueprint for survival of dehydration. P Natl Acad Sci USA.
2015;112:5833–7.
112. Zhu Y, Wang B, Phillips J, Zhang ZN, Du H, Xu T, Huang LC, Zhang XF, Xu
GH, Li WL, Wang Z, Wang L, Liu YX, Deng X. Global transcriptome analysis
reveals acclimation-primed processes involved in the acquisition of desiccation
tolerance in Boea hygrometrica. Plant Cell Physiol. 2015;56:1429–144.
113. FastQC. http://www.bioinformatics.babraham.ac.uk/projects/fastqc/.
Accessed 24 June 2014.
114. Hannon Lab. FASTX Toolkit. 2011. http://hannonlab.cshl.edu/fastx_toolkit/.
Accessed 24 June 2014.
115. Zerbino DR, Birney E. Velvet: algorithms for de novo short read assembly
using de Bruijn graphs. Genome Res. 2008;18:821–9.
116. Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, Amit I, Adiconis
X, Fan L, Raychowdhury R, Zeng Q, Chen Z, Mauceli E, Hacohen N, Gnirke A,
Rhind N, di Palma F, Birren BW, Nusbaum C, Lindblad-Toh K, Friedman N,
Regev A. Full-length transcriptome assembly from RNA-Seq data without a
reference genome. Nat Biotech. 2011;29:644–52.
117. Schulz MH, Zerbino DR, Vingron M, Birney E. Oases: Robust de novo RNA-
seq assembly across the dynamic range of expression levels. Bioinformatics.
2012;28:1086–92.
118. Gilbert D. EvidentialGene: tr2aacds, mRNA transcript assembly software.
2013. Accessed 30 June 2014http://arthropods.eugenes.org/EvidentialGene/.
Accessed 30 June 2014.
119. Slater GS, Birney E. Automated generation of heuristics for biological sequence
comparison. BMC Bioinformatics. 2005;6:31.
120. Li W, Godzik A. Cd-hit: a fast program for clustering and comparing
large sets of protein or nucleotide sequences. Bioinformatics.
2006;22:1658–9.
121. Conesa A, Götz S, García-Gómez JM, Terol J, Talón M, Robles M. Blast2GO: a
universal tool for annotation, visualization and analysis in functional
genomics research. Bioinformatics. 2005;21:3674–6.
122. The Gene Ontology Consortium. Gene Ontology Consortium: going forward.
Nucl Acids Res. 2015;43:D1049–56.
123. Lagesen K, Hailin P, Rødland EA, Staerfeldt HH, Rognes T, Ussery DW.
RNAmmer: consistent and rapid annotation of ribosomal RNA genes.
Nucleic Acids Res. 2007;35:3100–8.
124. Lowe TM, Eddy SR. tRNAscan-SE: a program for improved detection of
transfer RNA genes in genomic sequence. Nucleic Acids Res.
1997;25:955–64.
125. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical
and powerful approach to multiple testing. J Roy Stat Soc B Met.
1995;57:289–300.
126. Edgar RC. MUSCLE: a multiple sequence alignment with high accuracy and
high throughput. Nucleic Acids Res. 2004;32:1792–7.
127. Kearse M, Moir R, Wilson A, Stones-Havas S, Cheung M, Sturrock S, Buxton S,
Cooper A, Markowitz S, Duran C, Thierer T, Ashton B, Meintjes P,
Drummond A. Geneious Basic: an integrated and extendable desktop
software platform for the organization and analysis of sequence data.
Bioinformatics. 2012;28:1647–9.
128. Larkin MA, Blackshields G, Brown NP, Chenna R, McGettigan PA, McWilliam
H, Valentin F, Wallace IM, Wilm A, Lopez R, Thompson JD, Gibson TJ,
Higgins DG. Clustal W and Clustal X version 2.0. Bioinformatics.
2007;23:2947–8.
129. Emms DM, Kelly S. OrthoFinder: solving fundamental biases in whole
genome comparisons dramatically improves orthogroup inference accuracy.
Genome Biol. 2015;16:157.
130. Haas BJ, Papanicolaou A, Yassour M, Grabherr M, Blood PD, Bowden J,
Couger MB, Eccles D, Li B, Lieber M, MacManes MD, Ott M, Orvis J, Pochet
N, Strozzi F, Weeks N, Westerman R, William T, Dewey CN, Henschel R,
LeDuc RD, Friedman N, Regev A. De novo transcript sequence reconstruction
from RNA-Seq Reference Generation and Analysis with Trinity. Nature Protoc.
2013;8:1494–512.
Roberts and Roalson BMC Genomics (2017) 18:240 Page 25 of 26
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

131. Robinson MD, Oshlack A. A scaling normalization method for differential
expression analysis of RNA-seq data. Genome Biol. 2010;11:R25.
132. Capella-Gutiérrez S, Silla-Martínez JM, Gabaldón T. trimAl: a tool for
automated alignment trimming in large-scale phylogenetic analyses.
Bioinformatics. 2009;25:1972–3.
133. Felsenstein J. PHYLIP –Phylogeny Inference Package (Version 3.2). Cladistics.
1989;5:164–6.
134. Yang Z. PAML 4: phylogenetic analysis by maximum likelihood. Mol Biol
Evol. 2007;24:1586–91.
135. Al-Shahrour F, Díaz-Uriarte R, Dopazo J. Fatigo: a web tool for finding
significant associations of gene ontology terms with groups of genes.
Bioinformatics. 2004;20:578–80.
• We accept pre-submission inquiries
• Our selector tool helps you to find the most relevant journal
• We provide round the clock customer support
• Convenient online submission
• Thorough peer review
• Inclusion in PubMed and all major indexing services
• Maximum visibility for your research
Submit your manuscript at
www.biomedcentral.com/submit
Submit your next manuscript to BioMed Central
and we will help you at every step:
Roberts and Roalson BMC Genomics (2017) 18:240 Page 26 of 26
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

1.
2.
3.
4.
5.
6.
Terms and Conditions
Springer Nature journal content, brought to you courtesy of Springer Nature Customer Service Center GmbH (“Springer Nature”).
Springer Nature supports a reasonable amount of sharing of research papers by authors, subscribers and authorised users (“Users”), for small-
scale personal, non-commercial use provided that all copyright, trade and service marks and other proprietary notices are maintained. By
accessing, sharing, receiving or otherwise using the Springer Nature journal content you agree to these terms of use (“Terms”). For these
purposes, Springer Nature considers academic use (by researchers and students) to be non-commercial.
These Terms are supplementary and will apply in addition to any applicable website terms and conditions, a relevant site licence or a personal
subscription. These Terms will prevail over any conflict or ambiguity with regards to the relevant terms, a site licence or a personal subscription
(to the extent of the conflict or ambiguity only). For Creative Commons-licensed articles, the terms of the Creative Commons license used will
apply.
We collect and use personal data to provide access to the Springer Nature journal content. We may also use these personal data internally within
ResearchGate and Springer Nature and as agreed share it, in an anonymised way, for purposes of tracking, analysis and reporting. We will not
otherwise disclose your personal data outside the ResearchGate or the Springer Nature group of companies unless we have your permission as
detailed in the Privacy Policy.
While Users may use the Springer Nature journal content for small scale, personal non-commercial use, it is important to note that Users may
not:
use such content for the purpose of providing other users with access on a regular or large scale basis or as a means to circumvent access
control;
use such content where to do so would be considered a criminal or statutory offence in any jurisdiction, or gives rise to civil liability, or is
otherwise unlawful;
falsely or misleadingly imply or suggest endorsement, approval , sponsorship, or association unless explicitly agreed to by Springer Nature in
writing;
use bots or other automated methods to access the content or redirect messages
override any security feature or exclusionary protocol; or
share the content in order to create substitute for Springer Nature products or services or a systematic database of Springer Nature journal
content.
In line with the restriction against commercial use, Springer Nature does not permit the creation of a product or service that creates revenue,
royalties, rent or income from our content or its inclusion as part of a paid for service or for other commercial gain. Springer Nature journal
content cannot be used for inter-library loans and librarians may not upload Springer Nature journal content on a large scale into their, or any
other, institutional repository.
These terms of use are reviewed regularly and may be amended at any time. Springer Nature is not obligated to publish any information or
content on this website and may remove it or features or functionality at our sole discretion, at any time with or without notice. Springer Nature
may revoke this licence to you at any time and remove access to any copies of the Springer Nature journal content which have been saved.
To the fullest extent permitted by law, Springer Nature makes no warranties, representations or guarantees to Users, either express or implied
with respect to the Springer nature journal content and all parties disclaim and waive any implied warranties or warranties imposed by law,
including merchantability or fitness for any particular purpose.
Please note that these rights do not automatically extend to content, data or other material published by Springer Nature that may be licensed
from third parties.
If you would like to use or distribute our Springer Nature journal content to a wider audience or on a regular basis or in any other manner not
expressly permitted by these Terms, please contact Springer Nature at
onlineservice@springernature.com